In this paper, a novel framework for energy management in smart distribution networks is presented, which employs a hybrid fuzzy-genetic algorithm to solve a multi-objective optimization model. The proposed model is designed to reduce energy losses, improve voltage profiles, minimize operational costs, and mitigate environmental pollution, while effectively addressing the dynamic characteristics of load demand and uncertainties associated with renewable energy sources. The model incorporates the interaction among distributed generation resources, energy storage systems, and electric vehicles within a 24-hour scheduling framework. For optimization, a hybrid algorithm is utilized: fuzzy logic first identifies priority search regions, followed by the genetic algorithm extracting the optimal solution. Simulation results on standard 33-bus test systems demonstrate significant reductions in energy losses, enhanced voltage quality, and improved system efficiency compared to conventional methods. The proposed approach provides a flexible, efficient, and scalable solution, demonstrating high capability in addressing the complexities of future-oriented distribution networks.