The transportation sector is responsible for approximately \(23\%\) of global greenhouse gas (GHG) emissions, with road transportation contributing nearly \(70\%\) of these emissions. The widespread adoption of electric vehicles (EVs) is transforming this sector by reducing emissions and decreasing reliance on fossil fuels. However, the growing number of EVs presents significant challenges for charging infrastructure, particularly in managing long queues, extended wait times, and limited station capacity. Most existing studies on the performance of electric vehicle charging stations assume Poisson arrivals and exponential charging times, simplifications that often overlook real-world variability. This paper introduces a generalized queueing model that leverages empirical interarrival and charging duration data for more accurate performance evaluation. A transient analysis is conducted, examining two operational optimization strategies aimed at minimizing queue sizes during peak demand: (1) a queue management policy that encourages charging only up to a predefined state-of-charge (SoC) threshold instead of the typical 80–100%, and (2) dynamic control of the number of active charging ports based on demand. The results show that these operational optimization policies improve the efficiency of the charging station and significantly improve the customer experience.