Efficient forecasting fitness and evolutionary strategies are more critical for improving evolutionary algorithm optimization performance. Most current interactive evolutionary computation with large population has larger fitness estimation error and lower efficiency adopting the traditional evolutionary strategy. Accordingly, a fitness prediction method based on grey support vector regression and a set-based evolutionary strategy are proposed. Then, four comparative measures of set-based individuals evolution are defined, and adaptive crossover and mutation probability are proposed. Based on above strategies, a set-based interactive evolutionary algorithm is designed by using the powerful NSGA-II algorithm for optimizing tacit indices problems. The proposed algorithm is applied to RGB color One-max optimization problem, and its outstanding performance is experimentally demonstrated.