The uncertainty and randomness of the charging behavior of electric vehicles make a large number of charging loads connect to the grid in a short period of time, which will lead to large load fluctuations. At the same time, the disorderly charging behavior of electric vehicles can not guarantee the interests of charging users under the condition of time-of-use electricity prices. In order to alleviate the negative impact of these problems, the charging behavior of electric vehicles is firstly analyzed based on the Markov Decision Process (MDP) in the reinforcement learning. Secondly, an incentive function is constructed to guide the electric vehicle to make charging choice according to the power supply margin of the grid. Then an orderly charging strategy that meets the minimum load fluctuation and the minimum user cost at the same time is produced. Finally, the Monte Carlo method is utilized to simulate the charging status of electric vehicles. Results of orderly charging simulation show that the strategy can effectively improve the load superposition curve, play the role of peak shaving and valley filling and reduce the user charging cost.