The widespread adoption of electric vehicles (EVs) is reshaping urban energy consumption, with residential charging emerging as both a necessity and a technical challenge—especially in Italian multi-unit housing, which is the primary focus of this work. This thesis presents the development and laboratory testing of an intelligent Energy Management System (EMS) designed to optimize EV charging in condominium parking areas. Building on the work of previous thesis students, the proposed solution aims to minimize operational costs by aligning charging schedules with real-time electricity prices, while ensuring compliance with contractual power limits, vehicle necessities and user-defined constraints. At its core, the system relies on a Mixed Integer Linear Programming (MILP) optimizer, previously implemented in Python with Pyomo, which calculates optimal power setpoints for each charging point based on vehicle parameters, time constraints, and the Italian day-ahead energy price (PUN).