At present, most multi-objective optimization algorithms do not consider the interaction between decision variables, but just optimize all variables as a whole. With the increase of decision variables, the performance of multi-objective optimization algorithms will decrease sharply. Aiming at the above problems, a multi-objective optimization algorithm without parameter grouping and with large-scale variables (MOEA/DWPG) is proposed. By combining collaborative optimization with the decomposition-based multi-objective optimization algorithm (MOEA/D), this algorithm designs a grouping method without parameters to improve the grouping accuracy of interaction variables and to enhance the algorithm performance when it handles the multi-objective optimization problems with large-scale variables. Experimental results show that the proposed algorithm is significantly superior to MOEA/D and other advanced algorithms on the multi-objective problems with large-scale variables.