Most of the current multi-objective optimization algorithms adopt the bounded Pareto archive strategy based on the linear list structure, which has the drawbacks of Pareto fronts' oscillation, shrinking and other technical difficulties such as pre-determining relevant parameters. Therefore, this paper constructs a tree structure suitable for the storage and update of large-scale archive, replacing the linear structure to ensure high efficiency of archive maintenance and management. And a tree-structured unbounded archive strategy is proposed. We introduce population initialization based on orthogonal design, archive updating and optimal individual selection based on the tree structure into multi-objective particle swarm optimization, and propose a multi-objective particle swarm optimization algorithm based on tree-structured unbounded archive. Finally, simulation experiments on test functions verify the feasibility and effectiveness of the improved strategies and algorithm.