This paper presents a multi-objective optimization framework for energy management and trading in microgrids, focusing on the integration of Distributed Energy Resources (DERs) and Electric Vehicles (EVs). As the demand for sustainable energy solutions grows, the development of effective Energy Management Systems (EMS) that optimize energy flows while ensuring efficiency, reliability, cost-effectiveness, and sustainability becomes increasingly vital. We propose an advanced EMS that utilizes an enhanced Particle Swarm Optimization (PSO) algorithm to address challenges in optimal energy scheduling, cost reduction, revenue enhancement, battery health preservation, and EV user satisfaction. Additionally, the system incorporates demand response (DR) strategies and dynamic pricing mechanisms to improve operational efficiency and adaptability. The proposed approach is validated through a case study at the Green Energy Park (GEP) in Morocco, offering a real-world testbed. The results show that the EMS can reduce net costs by up to 42% compared to a baseline scenario, while optimizing renewable energy utilization and improving EV user satisfaction. This study provides valuable insights into the trade-offs involved in multi-dimensional decision-making for microgrid management and offers a comprehensive framework for future research in microgrid optimization and sustainable energy systems.