Real world optimization often involves noises and uncertainty. Most current research on evolutionary multiobjective optimization does not consider the effect of noise. This paper studies the performance of decomposition based multiobjective optimization evolutionary algorithm (MOEA/D) in noisy environment. Experiments are carried out to compare the performance of MOEA/D and NSGA II under different levels of noise in objective functions evaluation. Statistical analysis has been made to understand the behaviour of MOEA/D. Based on the comparison and analysis, we discuss possible improvement methods on MOEA/D for noisy optimization.