A novel two-layer path planning method for a cooperated ground vehicle (GV) and drone system is investigated, where the GV acts as the mobile platform of the drone and is used to conduct multiple area covering tasks collaboratively. The GV takes the drone to visit a set of discrete areas, while the drone takes off from the GV at potential nodes around each area and scans each area for collecting information. The drone can be recharged in the GV during the time when it travels between different areas. The objective is to optimize the drone’s scanning path for all areas’ coverage and the GV’s travel path for visiting all areas. A 0-1 integer programming model is developed to formulate the problem. A two-stage heuristic based on cost saving strategy is designed to quickly construct a feasible solution, then the Adaptive Large Neighborhood Search (ALNS) algorithm is employed to improve the quality of the solution. A simulation experiment based on the parks in Changsha, China, is presented to illustrate the application of the method. Random instances are designed to further test the performance of the proposed algorithm.