A cognitive system is presented, which is based on coupling a multi-objective evolutionary algorithm with a fuzzy information processing system. The aim of the system is to identify optimal solutions for multiple criteria that involve linguistic concepts, and to systematically identify a most suitable solution among the alternatives. The cognitive features are formed by the integration of fuzzy information processing for knowledge representation and evolutionary multi-objective optimization resulting in a decision-making outcome among several equally valid options. Cognition is defined as final decision-making based not exclusively on optimization outcomes but also some higher-order aspects, which do not play role in the pure optimization process. By doing so, the decisions are not merely subject to rationales of the computations but they are the resolutions with the presence of environmental considerations integrated into the computations. The work describes a novel fuzzy system structure serving for this purpose and a novel evolutionary multi-objective optimization strategy for effective Pareto-front formation serving for the goal. The machine cognition is exemplified by means of a design example, where a number of objects are optimally placed according to a number of architectural criteria.