The rapid spread of electric vehicles and the growing attention towards environmental sustainability is not always accompanied by adequate charging infrastructures, capable of meeting the growing demand and offering energy produced from renewable sources. The main challenges concern the planning, sizing and monitoring of charging stations to ensure an effective distribution of charging facilities capable of reducing waiting times and meeting the needs of different types of users. To address these challenges, it is essential to have tools capable of simulating different real-world scenarios and providing useful data for efficient planning of charging infrastructures. This work describes the design and implementation of a multi-agent simulator, developed in Python, which, by reproducing the individual behavior of single EV users in different scenarios, provides valuable information both to electric mobility service providers for the optimization of charging station management, and to policy makers as a support to local policy choices to improve urban mobility and encourage eco-sustainable behaviors of citizens.