Evolutionary Multi-Agent System approach for optimization (for multi-objective optimization in particular) is a promising computational model. Its computational as well as implemental simplicity cause that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS-such as presented in the course of this paper elitist extensions cause that results obtained with the use of proposed elEMAS (elitist Evolutionary Multi-Agent System) approach are as high-quality results as results obtained by such famous and commonly used algorithms as NSGA-II or SPEA2. Apart from the computational simplicity especially important and interesting aspects of EMAS-based algorithms it is characteristic for them a kind of soft selection which can be additionally easily adjusted depending on a particular situation-in particular it is possible to introduce auto-adapting selection into such systems. Such a kind of selection seems to be especially important and valuable in solving optimization tasks in uncertain or "noised" environments. In the course of this paper the model and experimental results obtained by elEMAS system in solving noisy multi-objective optimization problems are presented and the general conclusion is as follows: EMAS-based optimization system seems to be more effective alternative than classical (i.e. non agent-based) evolutionary algorithms for multi-objective optimization, in particular, in uncertain environment, it seems to be better alternative than NSGA-II algorithm.