Software architecture design can be regarded as finding an optimal combination of known general solutions and architectural knowledge with respect to given requirements. Based on previous work on synthesizing software architecture using genetic algorithms, we propose a refined fitness function for assessing software architecture in genetic synthesis, taking into account the specific anticipated needs of the software system under design. Inspired by real life architecture evaluation methods, the refined fitness function employs scenarios, specific situations possibly occurring during the lifetime of the system and requiring certain modifiability properties of the system. Empirical studies based on two example systems suggest that using this kind of fitness function significantly improves the quality of the resulting architecture.