In response to the growing integration of battery energy storage systems (BESS), electric vehicles (EV), and distributed generation (DG), planning frameworks have emerged to optimize the deployment of these units within distribution networks. In this context, this study introduces an artificial intelligence (AI) approach utilizing the genetic algorithm optimization technique to identify optimal installation nodes for BESS, EV charging stations, and DG units within the grid. The framework aims to minimize energy losses in the distribution system, incorporating a peak load shaving strategy in the BESS. The mathematical model encompasses technical and operational constraints from the resources and the grid. To address the complexity ensuing from exploring an extensive solution space, handling constraints, and managing the decision variables, a real coded genetic algorithm is employed to determine the optimal locations. Through simulations on a distribution feeder, the study demonstrates that resources installed based on the proposed approach significantly benefit the grid. This is evidenced by a reduction in energy losses, improved voltage levels, and demand mitigation during peak periods. An analysis of the BESS impact on losses, with respect to its installation nodes, is also conducted. Furthermore, the optimization approach is validated using benchmark functions and compared with another method.