The large-scale disordered integration of electric vehicles （EVs） into power grid poses many problems, such as enlarged power fluctuation for grid and increased charging cost for users. To solve these problems, this paper proposes an optimized EV charging method based on deep reinforcement learning （DRL）. Firstly, an EV ordered charging scheduling model is established aiming at minimizing the power fluctuation and user charging cost. Secondly, the EV charging behaviour is formulated as a Markov decision process （MDP） which evaluates the priority for each charging period based on load prediction information and time-of-use electricity tariff. The charging behaviour of EVs is controlled by the priority. The twin delayed deep deterministic policy gradient （TD3） algorithm is adopted to solve the MDP problem, which quickly optimizes EV ordered charging strategy. Finally, the effectivenesses of the proposed method in reducing charging cost and load fluctuation of distribution network are verified by case studies based on numerical examples.