Individuals’ fuzzy fitness is estimated according to user's evaluation time and single numerical fitness by using the large-scale population evolutionary optimization strategy. Individual phenotype similarity is calculated according to the individuals’ phenotype attributes and the width of individuals’ fuzzy finess. The population is divided into several clusters and the not assigned individuals’ fuzzy fitness is estimated by using individuals’ phenotypes similarity. The individuals’ choice fitness is constructed based on individuals’ fuzzy fitness and phenotype similarity to achieve individual similarity selection. The proposed algorithm is applied to a wall clock evolutionary design system, and compared with existing typical ones．The experimental results show that the proposed algorithm has advantages in improving optimization quality and alleviating user fatigue while improving its efficiency in exploration.