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Many increasingly complex multi-objective optimization problems are emerging, and there is an urgent need to develop new multi-objective optimization algorithms to meet the challenges. This study introduces the Multi-Objective Dung Beetle Optimization Algorithm (MODBO), which integrates competitive and neighborhood mechanisms to tackle such problems, Thanks to the dung beetle optimization algorithm’s fast convergence and robust optimization finding ability in single-objective optimization algorithms. The introduction of non-dominated sorting allows the Dung Beetle Optimization Algorithm to solve multi-objective optimization problems (MOPs). To make the Dung Beetle Optimization Algorithm maintain good search ability in searching, we introduce a Competition mechanism to guide the particles’ global optimal search and a Neighborhood mechanism to guide the particles’ local optimal value search. An external archive is introduced to make each generation positionally optimal. Finally, to analyze whether the MODBO algorithm’s improved strategy is effective, a comparison with the nine algorithms on CEC2020 was made, and the 3D sensor deployment problem was used to demonstrate that the MODBO algorithm can solve realistic problems.