The implementation of charging infrastructure for the electric vehicles (EV) can be costly and often complex to set up, moreover, the solutions are numerous and varied. We propose in this paper a discrete-event simulation software: ME Analytics, a techno-economic tool using discrete-event simulation (DES) to test and validate different charging solution strategies, make the most suitable investment and ensure the best quality of charging service. An efficient DES model is created to model the complex logistic flow. The model is illustrated by simulating a real case study. Once the current situation is understood, what-if scenarios can be used to establish ways of making decisions that result in charging stations performance improvement. The simulation results allow to answer the customer questions: First, what type of charging stations (CS) should be installed for the first CS (first wave of equipment to reach the quota targeted by the regulation in 2025, e.g., 5%)? Second, when will these first CS be insufficient, and what strategy to put in place to overcome this situation of saturation?