The disorderly charging of large-scale electric vehicles (EVs) will increase the operation risk of peak-up-peak to the power grid. In addition, as a mobile energy storage device, the disordered discharge of a large number of EVs will also have an important impact on the stability of the power grid. Therefore, it is necessary to guide the charge and discharge behavior of EVs in an orderly manner. First of all, the general loads of disordered charge and discharge of electric vehicles in a residential area are analyzed, and the daily load under different response is studied under the guidance of peak-valley time-of-use electricity price. On this basis, considering both the benefits of the divers and the power grid, the optimal charge and discharge model of the EVs are constructed, which takes the lowest charging cost of the EVs and the minimum peak-valley difference of the daily load in the community as the optimization objectives, and selecting the peak-valley time-sharing interval as the optimization variable. The optimal charge and discharge time intervals are found by Pareto-based optimization multi-objective genetic NSGA-Ⅱ and Pareto-based optimization particle swarm (PSO), respectively. The results by the different optimization algorithms are compared. Finally, the Monte Carlo algorithm is used to simulate and analyze the model. Simulation results show that users can reduce the charging cost to some extent through the discharge compensation, and NSGA-Ⅱ algorithm is better than PSO.