Microgrid optimization is essential for enhancing economic efficiency, ensuring reliable operation, and integrating renewable energy sources (RESs) into the power grid. However, the variability of renewable generation, fluctuating demand, and dynamic environmental conditions pose significant challenges to conventional optimization methods. This paper proposes an innovative energy management strategy based on the sand cat swarm optimization (SCSO) algorithm. Inspired by the adaptive hunting behavior of sand cats in harsh desert environments, SCSO offers strong robustness in addressing the complex, multi-dimensional, and nonlinear nature of microgrid optimization. The proposed strategy optimizes power allocation among photovoltaic systems (PVs), wind power plants (WPs), and combined heat and power plants (CHPs) to meet hourly demand while accommodating intermittent generation. Simulations on the IEEE 37-node system confirm the algorithm’s superior performance, with notable improvements in energy cost reduction, emission minimization, and renewable energy utilization, outperforming several benchmark optimization methods.