Aiming at the defect of poor global convergence of the regularity model-based multi-objective estimation of distribution algorithm, a multi-objective estimation of distribution algorithm based on adaptive Opposition-Based Learning (OBL) was proposed. In the algorithm, whether to carry out OBL was judged according to the change rate of the function. When the change rate of the function was small, the algorithm was easily to fall into the local optimum, so that OBL was performed to increase the diversity of individuals in current population. When the change rate of the function was large, the regularity model-based multi-objective estimation of distribution algorithm was run. In the proposed algorithm, with the timely introduction of OBL strategy, the influences of population diversity and individual distribution on the overall convergence quality and speed of optimization algorithm were reduced. In order to verify the performance of the improved algorithm, Regularity Model-based Multi-objective Estimation of Distribution Algorithm (RM-MEDA), Hybrid Wading across Stream Algorithm-Estimation Distribution Algorithm (HWSA-EDA) and Inverse Modeling based multiObjective Evolutionary Algorithm (IM-MOEA) were selected as comparison algorithms to carry out the test with the proposed algorithm on ZDT and DTLZ test functions respectively. The test results show that the proposed algorithm not only has good global convergence, but also improves the distribution and uniformity of solutions except on DTLZ2 function.