INTRODUCTION In the present day, hydraulic simulation models have become widely utilized for analyzing the behavior of water distribution systems (WDS), as noted by . The calibration of water distribution models involves adjusting network parameters, such as pipe roughness and nodal demand, to minimize the disparities between simulated results and real measurements. Over the last thirty years, calibration has been a popular research topic among WDS analysts, and there have been numerous publications on this subject in scientific and engineering literature. In their work, Savic et al. (2009) conducted a comprehensive review of the calibration of water distribution network models and classified the calibration methods into three categories. The first category involves iterative procedure models, where unknown parameters are updated at each iteration by solving the set of steady-state mass balance and energy equations using obtained water heads and/or flows at nodes. However, this approach tends to have a slow convergence rate and is only suitable for handling small-scale problems. The second category includes explicit models, also known as hydraulic simulation models, which rely on solving an extended set of steady-state equations that include initial equations and additional ones derived from available measurements. An objective function or cost function is typically applied to minimize the disparities between measured and model-predicted variables. However, this method requires a large quantity of observation data to accurately estimate calibration parameters. Nevertheless, simplifications of the model should be made to find a reasonable solution. The third category of calibration methods involves implicit models that are generally based on optimization techniques. The calibration variables for these models encompass a broad range of parameters, such as nodal demand and pipe roughness, or valve status and leak parameters., 2011). A variety of optimization methods have been employed to address the relevant calibration problem, including the general reduced gradient method, the Gauss-Newton method, the Levenberg-Marquardt method, the extended complex method of box, linear and non-linear programming, the Kalman filtering method, and the simulated annealing method. However, there are trade-offs and no general guidance exists regarding which optimization technique is preferable for a specific calibration problem. Various optimization techniques have been proposed for model calibration utilizing genetic algorithms (GAs). GAs have been shown to be efficient in assessing sensitivities, managing extensive calibrations, and integrating additional calibration parameter types and constraints into the optimization process. Recently, researchers have explored the use of evolutionary computer techniques to calibrate hydraulic models, with a focus on leakage estimation and water demand. However, the roughness coefficient is a primary parameter that contributes to uncertainty in model outputs, and different equations may yield vastly different estimates of frictional head losses, depending on the pipe size and water flow rate. The Darwin Calibrator in the commercial WaterGEMs has been developed utilizing GA to enable the adjustment of model parameters and modification of the roughness of pipe groups and junction demand during the calibration process. However, WaterGEM did not account for the spatial characteristics of pipes in WDS calibration. Regarding the previous requirements and limitations, this study proposes an enhanced method that employs Genetic Algorithm to optimize the roughness coefficient while incorporating the spatial factor and actual junction demand in the EPANET hydraulic model. Notably, EPANET is a freely available software that models the water quality and hydraulic behavior of water distribution piping systems. Furthermore, a case analysis is carried out in the study to illustrate how the proposed technique can enhance the operational effectiveness by minimizing the difference between the simulated and observed values. The proposed method is further compared to WaterGEMs to provides a reliable reference for the design and routing scenario of WDS.