The paper describes a procedure for simulating the recharging activities of private electric cars in urban areas, thus enabling the reconstruction of daily electric power demand profiles on a zonal basis. The procedure relies on a behavioral model, which is calibrated using data from a large Stated Preference (SP) survey. The results of this calibration, along with pre-processed Floating Car (FC) data, serve as inputs for a simulation model, allowing for the integration of both behavioral insights and real-world travel patterns. The procedure compares, with a “what – if” approach, different demand and supply scenarios, varying the distribution and technical characteristics of the electric cars fleet as well as the level of both private and public charging infrastructure in the study area. The goal is to identify charging supply networks that meet demand needs while minimizing installation costs and local power requirements. The procedure has been applied to a sample of approximately 10,000 cars circulating in the metropolitan area of Rome, for which it was possible to identify the area of residence. Future developments could involve re-conducting the survey to refresh model parameters, validating the results with actual charging data, and assessing the potential for smart charging and vehicle-to-grid (V2G) scenarios.