In this paper, we propose a new method for large scale multi-objective optimization based on symmetric points search and variable grouping, named SSVG. The main idea is to use variable grouping scheme first to divide the original decision space into several subspaces. In each subspace, the symmetric points of the points in population form some potential search directions. Using the search directions, the possibility of finding the optimal solutions will increase greatly. Moreover, in order to decrease the dimension of problem, a new transformation function which transforms the decision space into a lower dimension search space (weight vector space) is designed. Furthermore, experiments are conducted on some benchmarks with 200, 500 and 1000 decision variables and the proposed algorithm SSVG is compared with three state-of-the-art algorithms: MOEA/DVA, WOF and LSMOF. The results show that the proposed algorithm outperforms the compared algorithms in term of convergence and diversity.