To a certain extent, the difference in the location of geographical regions and the difference in functional attributes of regional grids result in the great difference in electric vehicle charging load within each grid. Because of the insufficient consideration of the difference in dynamic charging load distribution of electric vehicles in current research on electric vehicle charging demand forecasting, this paper proposes a data-driven gridding charging demand forecasting method for private electric vehicles considering the differences of geographical regions and the diversity of user trips. Firstly, data mining on the travel tracks of private electric vehicle users, the urban traffic network, and other data types is conducted. Mathematical models are constructed to obtain the origin-destination information of multi-stage trips and the basic travel patterns of private electric vehicle users. Secondly, the latitude and longitude coordinates of each point of interest (POI) are mapped to the geographic network based on the geographic information system platform. Various POI quantity sets combined with users’ daily travel purposes in the geographic area grid are classified. The natural classification method is adopted to implement accurate grid division of the studied geographic area. The functional area grid includes five categories: the work area, the business area, the living area, the residential area, and the mixed area. An origin-destination information probability matrix for each functional area is established in multiple periods. Combined with the obtained distribution results of private electric vehicles in each grid, this paper establishes an electric vehicle charging load forecasting model based on the Monte Carlo method to capture the continuous changes of electric vehicle electricity amount transferred between grids. Based on the actual historical data of electric vehicles in Suzhou, China, and taking a region of Suzhou as the application environment, the simulation of charging demand forecasting for private electric vehicles in each functional region is completed. The simulation results verify the rationality of regional grid division and the accuracy of charging demand forecasting.