As electric car penetration is higher and higher, the charging of large-scale electric vehicle will cause power grid load to a peak and peak phenomenon，burden to risk and the stable operation of power system. Therefore, how to shift peak load and reduce load fluctuations has become an important problem for power grids to solve the question of EV charging. In this paper, a real - time rolling optimization model is proposed, in which the upper optimize guidance curve and the lower realizes load real - time following. In upper master control center, based on typical daily routine load curve and vehicle travel forecast data, establishes a optimization with the minimum of total load (normal load and EV charging load) peak valley, draw a optimization guide curve; The lower intelligent control center calculates EV charging priority according to the return of EV charging status and demand, then guide electric vehicles to charge in order following the power guide curve optimized in the upper. Then, the conventional load data of a community and the probability model data of EV users travel were used to simulate the charging, and the related indicators were calculated and compared with the results of unordered charging. Orderly charge simulation results show that this control strategy can effectively achieve the EV charging load peak shift, to reduce the load fluctuation, at the same time also can increase the recharge of the agent service revenue, meet the requirements of charging users.