As a bridge between distributed photovoltaic (PV) panels and users, energy storage systems are crucial for balancing supply and demand in village-level microgrids (VMG). However, the current mainstream energy-based lithium battery energy storage system faces the problem of shortened lifetime due to frequent charging and discharging operations. To address this, a hybrid energy storage scheme integrating supercapacitors (SC) and lithium batteries is designed for VMG, and a hybrid energy storage scheduling strategy is formulated, aiming at optimizing the resource allocation of “source-load-storage” to effectively cope with the fluctuation of power supply and demand, and at the same time, reducing the loss of lithium batteries. In order to make full use of the active support capability of supercapacitors and reduce the fluctuation of grid voltage and frequency, an optimization objective of maximizing the sum of virtual inertia time constants of supercapacitors is set in the scheduling strategy. In addition, in order to solve this high complexity model efficiently, a Multi-objective snow geese optimization algorithm (MSGAR) based on enhancing the adaptive behavior of the population and incorporating an external warehouse mechanism is developed. Finally, the effectiveness of the proposed multi-objective optimization algorithm is verified by a case study. The results show that the proposed algorithm exhibits more superior convergence performance on multi-objective benchmarking functions compared to existing evolutionary algorithms.