This paper investigates the use of multi-objective evolutionary algorithms to solve single-objective optimization problems, and an instructional theory is proposed for constructing reasonable additional objective. A multi-objective
optimization algorithm based on multi-objectivization is also proposed. Single-objective problems are changed to multi-objective problems based on individual diversity. The nondominated sorting genetic algorithm is used as multi-objective optimization algorithm. The computation results show that the proposed algorithm has the approving performance in maintaining population diversity, dealing schema deception problems and dynamic optimization.