Traditional centralized optimization methods for electric vehicles (EVs) are faced with problems such as scheduling difficulty, a large amount of computation and lack of real data support in practical application, and they cannot accurately reveal the interaction behavior among various entities. Therefore, a Stackelberg game optimization scheduling strategy for aggregated EVs considering user satisfaction and the road network is proposed. First, it simulates user travel behavior based on real travel data and road network data. Second, the load aggregator (LA) integrates EV load resources to cluster EVs with similar travel characteristics. In the two-level Stackelberg game model, the LA is the leader of the upper level, and the clustered EV subgroups are the followers of the lower level. Considering the different consumption preferences of EV users, Nash equilibrium is achieved by optimizing the pricing strategy of the LA, the output plan of new energy and energy storage systems, and the charging and discharging strategies of EV clusters. The solution is achieved by an improved genetic algorithm. Finally, simulation is used to verify that the proposed model can effectively improve revenue of the LA and consumer surplus of EV users, increase consumption of new energy, and provide differentiated services for users with different consumption preferences.