Abstract: In multi-objective optimization, balancing both convergence and diversity in the searching population is paramount. However, the AR-MOEA algorithm, which prioritizes optimizing the IGD-NS indicator, and accelerates the convergence process, is prone to trapping in local optima, leading to incomplete coverage of the entire Pareto front by the population. To address this issue, this paper introduces the Adaptive Reference Point Multi-Objective Evolutionary Algorithm based on Grid Crowding (AR-MOEA-GC). This algorithm differentiates the fitness calculation methods for contributing and non-contributing individuals within the population, thereby ensuring both diversity and convergence. Furthermore, to expedite the convergence speed of the population during the later stages of the algorithm, a reference point adjustment strategy is integrated to guide the population to evolve towards the true Pareto front. The enhanced algorithm was rigorously tested against six advanced multi-objective evolutionary algorithms using three types of test functions. The results demonstrate that AR-MOEA-GC exhibits competitive performance in solving three-dimensional multi-objective optimization problems.