In this study, Electric Multiperiodic Technician Routing and Charging Station Location Problem is defined. A series of preventive and corrective maintenance-repair and spare parts supply services are offered to customers located in different geographical regions of the problem. In addition, the customers can request these tasks in different time windows throughout the planning horizon. Teams of technicians with different competencies are formed and allocated to these tasks that are provided in the customer locations. Considering that the workforce scheduling and routing problem is in the NP-hard class, thus the defined problem is also in the same class. Unlike the literature, in this study, it is taken into account that technicians reach customers with electric vehicles instead of using conventional internal combustion engine vehicles. In this way, it will be possible to reduce the use of fossil fuels and the environmental impact of these fuels due to transportation. In addition, due to the operating costs of electric vehicles are lower than those of conventional vehicles, electric vehicles emerge as an economical option for businesses. In addition to their advantages, the limited range of electric vehicles makes them dependent on using charging stations. For this reason, routing plans need to be made efficiently in order for these vehicles to compete with conventional vehicles. In the proposed problem, while creating a daily schedule and route for the teams throughout the planning horizon, tracking the state of charge of the vehicles and determining the location of the charging station are also considered. In addition, the legal rest periods of the technicians are also taken into account. The problem is modelled with the mixed integer programming formulation. Furthermore, the data set generated from the real-life instances. In order to solve the problem variable neighbourhood search heuristic is used. Computational comparisons are conducted to compare the performance of the heuristic. The results indicate that it can find the optimum solutions. Moreover, the heuristic is able to produces better results than the CPLEX solver in a reasonable time.