As an emerging load, there will be a series of impacts from electric vehicles connected largely to the power grid, such as power quality, frequency stability and voltage stability. Through analysis of the existing time-sharing electricity price system, this paper establishes a multi-objective optimization scheduling model by adopting the minimization of mean square error and peak and off-peak difference system load as the objective function in terms of power distribution network. It also uses the minimum of battery charging and discharging cost of electric car users and the minimum of battery loss as the objective function from the aspect of users. A cloud adaptive particle swarm optimization algorithm is applied in this paper. The simulation results show that the dispatching strategy is suitable for a time-sharing tariff, and compared with the fixed electricity price, the time-sharing electricity price optimizes the system load and reduces the user cost at the same time. The time-sharing electricity price 2, where the peak-valley price difference is larger and the mean value is higher, increases slightly the user cost compared with the time-sharing electricity price 1, but the peak-regulating capacity of the power grid is evidently enhanced. This work is supported by National Natural Science Foundation Project of China (No. 61761049) and Scientific Research Foundation Project of Yunnan Education Department (No. 2019Y0168) and Yunnan Applied Basic Research Youth Project (No. 2018FD052).