This paper presents the optimization process and its simulation tool, for the optimization of the control parameters in the cooling appliance. The simulation tool simulates temperatures inside the cooling appliance at different modes of regulation. In our appliance some of the cabinets have a common cooling system, which means that the regulation of the cabinets is interdependent. The result of simulation consists of data, which is used during the optimization process to evaluate each found parameter setting. The optimizer uses an evolutionary heuristic search approach to find the optimal set of control parameters iteratively over evolving generations. The approach is based on probabilistic methods to decide on changes and the direction of search. The aim was to use a parameter-less algorithm that is able to find optimal, or at least very good solutions, relatively quick, and without the need for an algorithm parameter setting specialist. The implemented evolutionary algorithm, with the origins in genetic algorithm, does not need any predefined control parameters values. We were able to find a set of control parameters of a cooling appliance, that give an optimal performance with the lowest possible energy consumption. Additionally, we found out that the results of the optimization, resulting in one prototype, do not apply to another. Namely, change of the characteristics of the appliance and its thermal responses, also change the optimal settings. Nevertheless, if we find out that some components bring improvement to the appliance, they are further optimized; but those without influence to the appliance performance are omitted, which leads to cost reduction. This is some kind of the evolutionary selection of the reliable and robust components.