<p>The design of renewable-powered mining microgrids is an essential study for the shift towards electric and carbon-neutral operations within the mining sector. This paper presents a multi-objective two-stage optimisation framework for the planning of microgrids in remote mine sites. The first-stage (strategic) problem aims to minimise the total net present cost, total greenhouse gas emissions, renewable energy curtailment, and improve system reliability. In the second-stage (scheduling) problem, the energy scheduling of the microgrid’s energy sources is determined using a rule-based approach. The proposed framework provides optimal sizing for renewable energy sources, energy storage systems, and fossil-fuel backup generators to meet the microgrid’s electricity demand, demonstrating the feasibility and benefits of renewable-based microgrid deployment in mining operations. To solve this complex problem, several state-of-the-art multi-objective evolutionary algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II), the NSGA-III, the unified NSGA-III (U-NSGA-III), S-metric selection evolutionary multi-objective algorithm (SMS-EMOA), and the adaptive geometry estimation multi-objective evolutionary algorithm (AGE-MOEA) and its newer version AGE-MOEA-II, are applied to efficiently explore the performance of these techniques in finding the best trade-off between competing objectives. The effectiveness of these algorithms is evaluated and compared in the context of a real-world mining case study. The simulation results demonstrate that SMS-EMOA and AGE-MOEA outperform other algorithms in terms of convergence and diversity, particularly for complex microgrid configurations. The study highlights the potential of renewable-based microgrids to significantly reduce emissions, with trade-offs in cost, depending on the inclusion of energy storage solutions.</p>