Electric vehicles (EVs) are the most prominent technology for reducing road transport emissions. Their rapid adoption necessitates efficient deployment of charging infrastructure. This study presents works in progress in an innovative approach integrating simulation and artificial intelligence (AI) to optimize EV charging station placement. We develop a microscopic traffic simulation model combining cellular automata and agent-based techniques to capture the dynamic interactions between vehicles, charging stations, and urban environments. Genetic algorithms and neural networks are employed to navigate the complex solution space and identify optimal configurations. The model considers multiple factors including traffic patterns, air quality, grid impacts, and urban mobility. The objective of this holistic approach is to improve charging infrastructure planning, leading to reduced congestion, lower emissions, and enhanced urban mobility. We discuss achievements to date and outline future research directions.