The use of electric vehicles (EV) is quite a lot. With so many EVs scattered, it is necessary to plan the placement of a filling station that takes into account all the components of tractive effort, regenerative braking, and parasitic power users. Actual driving distance and altitude data from Google Maps are used as data for placement of filling stations and can therefore far more accurately predict the range that can be achieved from a given EV than a typical Euclidean distance model. In addition, the optimization model for filling station placement considers the number of affordable households in the filling station procurement plan. One problem in this study is the importance of meeting the increasing demand for EV fuels. Considerations for adjusting existing EV levels. The proposed optimization technique is applied to the transportation network, and in the case study in the Solo area, where the focus is to reach the maximum range with the minimum number of filling station distances. The results are promising and show that flexibility, smart route selection, and numerical efficiency of the proposed design techniques, can choose strategic locations to fill stations from thousands of possible locations without numerical difficulties.