Cooperative Intelligent Transportation Systems (C-ITS) can enhance urban network performance and facilitate smoother traffic flows. Green Light Optimal Speed Advisory (GLOSA) is a subset of C-ITS that aims to reduce unnecessary stops by recommending optimal speeds to vehicles that allow them to avoid red lights. While recent studies have confirmed its theoretical benefits at single intersections, applying GLOSA in real-world conditions and on an urban network scale remains underexplored. This study seeks to address the location problem for implementing GLOSA systems by proposing a model that identifies the best locations for deployment. The model optimizes network performance based on several factors: travel time, waiting time, vehicle emissions, and energy consumption. It is structured as a bilevel optimization model, where the upper level optimizes infrastructure locations and the lower level simulates traffic flows. A heuristic algorithm, which is a genetic-based algorithm, is proposed to address the large-scale nature of the problem. The proposed algorithm is applied at the upper level to solve the location problem and provide a near-optimal solution. The SUMO traffic simulator is used for microsimulations at the lower level. This approach has been tested through a case study on the Luxembourg urban network. Its results indicate that it delivers an effective solution for improving network performance and implementing systems in all locations might not be the best or a near-optimum solution. The study also demonstrates that poorly chosen locations for GLOSA implementation can adversely affect traffic flows and reduce the overall network's functionality.