This study proposes a comprehensive model for optimizing the location and capacity planning of electric vehicle charging stations (EVCSs). The model integrates location modeling with demand forecasts and market penetration to determine optimal locations and capacities for EVCSs. A genetic algorithm is used to solve the p-median problem for location selection, while Arena 14 simulation software is used to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that the model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. The study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system. The study also highlights the importance of considering the number of EVs entering the market and user charging behaviors in determining station capacities. The results show that the number of charging units required at the stations decreases as the number of stations increases. The study concludes that the model provides a practical and effective solution for locating EVCSs and determining their capacities, considering the gradual entry of EVs into the market and consumers' charging behavior. The study also highlights the importance of balancing waiting times at EVCSs by changing the type of unit at the stations, as demonstrated in previous studies. The study also emphasizes the need to optimize the normal and fast charging modules of EVs to effectively reduce the burden on the distribution network caused by EVs. The study is limited by the lack of an EVCS system in Turkey, but it provides a comprehensive model for optimizing the location and capacity planning of EVCSs.This study proposes a comprehensive model for optimizing the location and capacity planning of electric vehicle charging stations (EVCSs). The model integrates location modeling with demand forecasts and market penetration to determine optimal locations and capacities for EVCSs. A genetic algorithm is used to solve the p-median problem for location selection, while Arena 14 simulation software is used to model station traffic and optimize charging unit types and quantities. The model prioritizes public areas, considering potential demand points and station locations to propose optimal charging areas. Results indicate that the model minimizes travel distances and waiting times, offering a scalable solution adaptable to future EV market growth. The study contributes to the field by presenting a sustainable and economical model for EVCS placement and capacity planning, underlining the importance of a robust charging network in the broader adoption of electric transportation. The findings suggest that proactive infrastructure development, guided by accurate demand predictions and optimized location strategies, can significantly enhance the feasibility and attractiveness of EVs, supporting global efforts towards a cleaner, more sustainable transportation system. The study also highlights the importance of considering the number of EVs entering the market and user charging behaviors in determining station capacities. The results show that the number of charging units required at the stations decreases as the number of stations increases. The study concludes that the model provides a practical and effective solution for locating EVCSs and determining their capacities, considering the gradual entry of EVs into the market and consumers' charging behavior. The study also highlights the importance of balancing waiting times at EVCSs by changing the type of unit at the stations, as demonstrated in previous studies. The study also emphasizes the need to optimize the normal and fast charging modules of EVs to effectively reduce the burden on the distribution network caused by EVs. The study is limited by the lack of an EVCS system in Turkey, but it provides a comprehensive model for optimizing the location and capacity planning of EVCSs.