This paper presents an integrated route planning algorithm to provide optimal routes and corresponding charging schemes for EVs (Electric vehicles) users with different travel objectives based on spot price and traffic conditions. With the development of EVs, more users are facing difficulties to find a charging route that satisfy their demands. To solve the problem, the route planning algorithm is improved based on classified travel objectives, meanwhile the spot price forecast model is established to provide the user's economic assessment. Firstly, the classification of user's travel objectives is completed and the evaluation indicators are proposed. Secondly, a time-window electricity price forecasting based on the GRU (Gated recurrent unit) neural network is established to generate pricing information for route planning algorithm. Finally, SAA (Simulated annealing algorithm) is combined with A * (A-star) algorithm and Dijkstra algorithm to gain the integrated route planning algorithm. Also, the algorithm provides the users with optimal charging paths considering travel objective and price prediction. The results of the optimal routes are displayed on App (application) so that the users can choose their own charging paths and control EV's charging at any time. The simulations prove the effectiveness and accuracy of the proposed algorithm.INDEX TERMS Electric vehicle, spot price, route planning, travel objective, gated recurrent unit, A * algorithm, simulated annealing algorithm.