Multiobjective evolutionary algorithms (MOEAs) have received much attention in multiobjective optimization in recent years due to their practicality. However, with limited computational resources, most existing MOEAs can not solve large-scale multiobjective optimization problems (LSMOPs), a class of problems that are widespread in the real world. To improve the ability of MOEAs to deal with LSMOPs, this paper innovatively proposes a dual decomposition strategy (DDS). Firstly, the Outer Decomposition uses a sliding window to divide large-scale decision variables into overlapped subsets of small-scale ones. A small-scale multiobjective optimization problem (MOP) is generated every time the sliding window slides. Then, once a small-scale MOP is generated, the Inner Decomposition immediately creates a set of global direction vectors to transform it into a set of single-objective optimization problems (SOPs). At last, all SOPs are optimized by adopting a block coordinate descent strategy, which guarantees the solution’s integrity and optimality during optimization. The proposed DDS can be embedded into many existing MOEAs to improve their performance for solving LSMOPs. Compared with four state-of-the-art evolutionary algorithms, the experimental re- sults also show the remarkable efficiency and solution quality of the proposed DDS in solving LSMOPs. In addition, experimental on two real-world problems show that DDS can achieve the best performance beyond at least one order of magnitude with up to 3072 decision variables.