<p>Grinding is a critical step in the gear manufacturing process chain, yet it is associated with high energy consumption, particularly due to the significant contributions of grinding energy and coolant supply energy. To reduce energy consumption, current research primarily focuses on reducing machining time and optimizing equipment, but lacks comprehensive studies on combined process parameter optimization. This study proposes a multi-objective optimization approach to minimize grinding energy consumption and coolant supply. An optimization model was developed, incorporating variables such as wheel speed, axial feed rate, axial grinding depth, and coolant flow rate. Single-factor and two-factor interaction experiments were conducted to analyze parameter effects, followed by orthogonal experiments to establish a multivariate nonlinear regression model with fitting accuracies of 0.827 and 0.892. The NSGA-II algorithm, combined with the TOPSIS method, was employed for model solving, outperforming MOPSO and MOGA in terms of P-value, HV-value, and optimization results. The proposed method achieved reductions of 16.01% in grinding energy consumption and 33.72% in coolant supply compared to conventional methods. This research provides a scientific approach for process parameter optimization in gear grinding, contributing to energy-efficient manufacturing. However, challenges related to machining complexity and model generalization require further investigation for broader applicability.</p> Graphical abstract <p></p>