Designing the way a complex system should evolve to better match the customers’ requirements provides an interesting class of applications for muticriteria techniques. The required models to support the improvement design of a complex system must include both preference models and system behavioral models. A MAUT model captures the decisions related to customers’ preferences whereas a fuzzy representation is proposed to model the relationships between systems parameters and performances to capture operational constraints. This latter part of the improvement design is supported by a branch and bound algorithm to efficiently compute the most relevant actions to be performed.