This work proposes a multi-objective meta-heuristic algorithm-based approach to optimize the positioning and capacity of photovoltaic (PV), wind, and biomass power generation units. It considers seasonal demand variations and power generation uncertainties while addressing multiple objectives within the power delivery network. These objectives include minimizing power loss and voltage deviation, maximizing generation cost savings, and reducing CO2 emission while adhering to equality and inequality constraints. The proposed method integrates a whale optimization algorithm (WOA) with probabilistic models to design a grid-integrated hybrid renewable energy system (HRES) that utilizes local resources to meet load demand. The effectiveness of the proposed approach is demonstrated on a standard IEEE 33-bus radial distribution test system. Additionally, a comparative study shows that WOA can provide a more optimal HRES design.