In recent years, aesthetic design is given increasing importance in the development of products industry with the growing maturity of product functions. The designer is required to reflect consumer needs in the aesthetic design while giving consideration to use and function. Effective techniques enabling design creation based on consumer preference and needs are indispensable. This study thus aims to construct a design support system which can identify various consumer needs and provide ideas to the designer at an early stage in the design process. In the identification of the consumer preferences, it is necessary to also expose vague consumer preferences. The design support system thus also aims to reduce burden on the consumers during consumer survey and expose consumer preference by using the genetic algorithm (a type of Interactive Evolutionary Computing) for the extraction of consumer preference. The authors also propose the use of rough sets and decision rules for analyzing the acquired consumer preference data specifically and effectively, and formulate consumer preference rules. Furthermore, the constructed system is able to generate multiple design solutions automatically by reflecting the consumer preference rules in design solutions created by the designer, and display the generated solutions to the designer.