Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has become increasingly popular in solving multi-objective problems (MOPs). In MOEA/D, weight vectors are responsible for maintaining a nice distribution of Pareto optimal solutions. Often, we expect to obtain a set of uniformly distributed solutions by applying a set of uniformly distributed weight vectors in MOEA/D. In this paper, we argue that uniformly distributed weights do not produce uniformly distributed solutions, however, uniformly distributed search directions do. Moreover, we propose to perform a pre-organization procedure before running MOEA/D. The procedure matches each weight to its closet candidate solution. Experimental results show (i) MOEA/D with uniformly distributed search directions would exhibit a better diversity performance, and (ii) MOEA/D with the pre-organization procedure performs better, especially for the convergence performance.