Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary algorithms. In this paper, two operators, biased initialization and biased crossover, are proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estima- tion of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial pop- ulation; biased crossover combines the location information of some best solutions and globally statistical information in the current population. Experiments have been conducted to study the effects of these two operators.