To address the challenges of single energy structure， high carbon emissions， and insufficient power supply stability in remote and deep-sea aquaculture areas， this study proposes a microgrid optimal scheduling method based on an Improved Multi-Objective Mantis Search Algorithm （IMOMSA）， aiming to provide a green and efficient energy supply solution for aquaculture. First， a multi-objective optimization scheduling model incorporating wind-solar hybrid complementary energy is established by comprehensively considering both economic efficiency and environmental sustainability. Second， to overcome the limitations of the original MOMSA algorithm， such as insufficient solution diversity and premature convergence， Bernoulli chaotic mapping is employed to enhance the diversity of the initial population， an artificial bee colony search strategy is introduced to strengthen local exploitation， and a lens imaging-based and opposition-based learning mechanism is integrated to improve global search performance. Finally， simulation and validation are conducted using a real-world aquaculture base in Chongming District， Shanghai.The results show that， compared to the original MOMSA， the IMOMSA reduces the GD and Spacing metrics by 78.45% and 10.71%， respectively， while increasing the HV metric by 100.00%. Meanwhile， the proposed method reduces economic costs and pollutant emissions by 1.76% and 22.62%， respectively. The study demonstrates that the proposed IMOMSA effectively enhances the comprehensive performance of microgrid scheduling solutions， achieving a synergistic optimization of economic and environmental objectives. This work provides theoretical and technical support for the efficient operation of multi-energy complementary aquaculture microgrids and holds significant practical implications for promoting the green transformation， upgrading， and sustainable development of the aquaculture industry.