Most real-world optimization problems behave stochastically. Evolutionary optimization algorithms have to cope with the uncertainty in order to not loose a substantial part of their performance. There are different types of uncertainty and this thesis studies the type that is commonly known as noise and the use of resampling techniques as countermeasure in multi-objective evolutionary optimization. Several different types of resampling techniques have been proposed in the literature. The available techniques vary in adaptiveness, type of information they base their budget decisions on and in complexity. The results of this thesis show that their performance is not necessarily increasing as soon as they are more complex and that their performance is dependent on optimization problem and environment parameters. As the sampling budget or the noise level increases the optimal resampling technique varies. One result of this thesis is that at low computing budgets or low noise strength simple techniques perform better than complex techniques but as soon as more budget is available or as soon as the algorithm faces more noise complex techniques can show their strengths. This thesis evaluates the resampling techniques on standard benchmark functions. Based on these experiences insights have been gained for the use of resampling techniques in evolutionary simulation optimization of real-world problems.