Currently, multiobjective evolutionary algorithm has been applied widely in various fields, and become one of the most attractive topics in the optimization area. This paper analyzes the deficiency of traditional multiobjective evolutionary algorithms in maintaining population diversity, and further proposes an objective space division based adaptive multiobjective evolutionary algorithm (SDA-MOEA) to solve multiobjective optimization problems. The proposed algorithm divides the objective space of a multiobjective optimization problem into a series of subspaces. During the evolution process, each subspace in SDA-MOEA can maintain a set of non-dominated solutions to guarantee the population diversity. Besides, SDA-MOEA self-adaptively distributes the evolutionary opportunities for each subspace according to its forward distance, which can promote the population convergence. Finally, 14 multiobjective problems of three groups are selected to measure the performance of SDA-MOEA. By comparing with five existing multiobjective evolutionary algorithms, the experimental results demonstrate that SDA-MOEA shows obvious superiority over these existing algorithms on 10 problems.