In order to improve the performance of Grasshopper Optimization Algorithm (GOA) in solving multi-objective problems, a Hybrid Multi-objective Grasshopper Optimization Algorithm (HMOGOA) based on fusion of multiple strategies was proposed. First, the Halton sequence was used to establish the initial population to ensure that the population had an uniform distribution and high diversity in the initial stage. Then, the differential mutation operator was applied to guide the population mutation, so as to promote the population to move to the elite individuals and extend the search range of optimization. Finally, the adaptive weight factor was used to adjust the global exploration ability and local optimization ability of the algorithm dynamically according to the status of population optimization, so as to improve the optimization efficiency and the solution set quality. With seven typical functions selected for experiments and tests, HMOGOA were compared with algorithms such as multi-objective grasshopper optimization, Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA Ⅱ). Experimental results indicate that compared with the above algorithms, HMOGOA avoids falling into local optimum, makes the distribution of the solution set significantly more uniform and broader, and has greater convergence accuracy and stability.