In this article, we will optimize energy management for a hybrid system that combines renewable energy sources (solar) with storage systems (batteries), as well as residual loads and electric vehicles. This system is integrated into the traditional electricity network. The main objective is to develop energy management strategies that minimize costs, maximize the use of photovoltaic panels, and ensure a reliable energy supply. This includes the optimal management of energy flows between different sources and storage, utilizing the Genetic Algorithm (GA). The GA employs techniques such as selection, crossover, and mutation to find optimal solutions to management problems. The results demonstrate how the Genetic Algorithm can optimize the energy management of a hybrid system by balancing photovoltaic production, load consumption, electric vehicle usage, and exchanges with the electricity network. This leads to minimized costs and efficient battery charge management, ensuring optimal system performance.