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In this paper, a dynamic multiobjective particle swarm optimization (DMOPSO) method is presented for the optimal design of hybrid renewable energy systems (HRESs). The main goal of the design is to minimize simultaneously the total net present cost (NPC) of the system, unmet load, and fuel emission. A DMOPSO‐simulation based approach has been used to approximate a worthy Pareto front (PF) to help decision makers in selecting an optimal configuration for an HRES. The proposed method is examined for a case study including wind turbines, photovoltaic (PV) panels, diesel generators, batteries, fuel cells, electrolyzer, and hydrogen tanks. Well‐known metrics are used to evaluate the generated PF. The average spacing and diversification metrics obtained by the proposed approach are 1386 and 4656, respectively. Additionally, the set coverage metric value shows that at least 67% of Pareto solutions …