Aiming at the scheduling optimization problem of hybrid energy storage microgrid, a multi-objective optimization model with economic benefit and pollution treatment cost under grid-connected state is established. Based on the basic fireworks algorithm and the grey entropy parallel analysis theory, a multi-objective grey entropy fireworks algorithm is proposed. The proposed algorithm can effectively handle the conflict relationship between different objectives by assigning different entropy weights to the two studied objectives. The grey entropy parallel correlation degree is adopted as the fitness of fireworks algorithm to select excellent individuals and guide the algorithm to better search region. Simulation results show that the performance of the proposed multi-objective grey entropy fireworks algorithm is significantly better than that of the random weight-based and Pareto-based fireworks algorithm, and better than that of the classical NSGA-Ⅱ algorithm, which verifies the effectiveness of the established multi-objective model and proposed multi-objective algorithm.