Range anxiety continues to be one of the most important factors negatively affecting the transition to electric vehicles (EVs). However, range anxiety can be reduced if the EV driver knows where to stop for charging on the route according to the range information remaining at the beginning of the journey. Although EVs can access real-time data, it cannot be expected that the created charging plan will reduce range anxiety unless range prediction is made considering the conditions of the determined route. The aim of this study is to determine where and how much the EV should be charged for minimum travel time or cost by creating a charging plan based on real-time range prediction for a specified route. The deep neural network model was trained by using the inputs of the static features and dynamic features of the journey in the range prediction model. The amount of energy consumed between the nodes is obtained by the range prediction model. Within the scope of charging planning, a mixed integer programming model is developed that consider nonlinear charging times, dynamic charging prices, vehicle to the grid, multiple charger with different power levels and their availability. Since the developed mathematical programming model was insufficient in terms of the solution time, a matheuristic is proposed. Test results on 32 various problems indicate that the matheuristic approach outperforms the metaheuristic and heuristics for both minimum travel time and cost.