Electric vehicles are vehicles that use electric power instead of fossil fuels for propulsion, thereby reducing harmful emissions and contributing to a more sustainable lifestyle. The decrease in the cost of electric vehicles and advancements in battery technology have contributed to their increasing popularity. The new generation of these vehicles offers drivers greater autonomy and faster charging times, further promoting their usage. The aim of this project is to generate new optimal locations for electric vehicle charging stations (EV) using Python 3.8.10 and QGIS 3.16.14. The project is based on the code written by Obed Sims, which can be found at the following link: https://towardsdatascience.com/where-should-we-be-placing. By utilizing Python and the QGIS tools, the project aims to analyze various factors such as traffic density, charging demand and availability of potential locations as parking lots. These factors will be considered in identifying optimal locations for new charging stations by using MILP method programmed in Python. The generated locations will maximize the utilization of charging stations and provide benefits to the local community. Cities like Zagreb recognize the importance of developing charging infrastructure to support the growth of electric mobility. Investments in a network of fast charging stations along major traffic corridors and strategic locations within the city help reduce barriers for electric vehicle owners and encourage a transition to more sustainable forms of transportation.