In the existing optimization of Electric power material Vehicle Routing Problem （EVRP）， the objective function is relatively single， the constraints are not comprehensive enough， and the traditional solution algorithms are not efficient. Therefore， a multi-objective routing optimization model and solution algorithm for electric power material distribution based on Deep Reinforcement Learning （DRL） was proposed. Firstly， the electric power material distribution area constraints such as the distribution of gas stations and the fuel consumption of material transportation vehicles were fully considered to establish a multi-objective power material distribution model with the objectives of the minimum total length of the power material distribution routings， the lowest cost， and the highest material demand point satisfaction. Secondly， a power material distribution routing optimization algorithm DRL-EVRP was designed on the basis of Deep Reinforcement Learning （DRL） to solve the proposed model. In the algorithm， the improved Pointer Network （Ptr-Net） and the Q-learning algorithm were combined to form the Deep Q-Network （DQN）， which was used to take the sum of the negative value of the cumulative incremental routing length and the satisfaction as the reward function. After DRL-EVRP algorithm was trained and learned， it can be directly used for the planning of electric power material distribution routings. Simulation results show that the total length of the power material distribution routing solved by DRL-EVRP algorithm is shorter than those solved by the Extended Clarke and Wright （ECW） saving algorithm and Simulated Annealing （SA） algorithm， and the calculation time of the proposed algorithm is within an acceptable range. Therefore， the power material distribution routing can be optimized more efficiently and quickly by the proposed algorithm.
