In this paper, we propose a new multi-objective optimization method based on a functional specialization search strategy. The functional specialization search strategy is composed of two ideas. The first idea is to evaluate the progress status of search points and classification of search points. The second idea is to use the status to update each search point by suitable one of the Pareto-ranking or scalarization. The proposed method takes advantage of this search strategy to realize an efficient improvement both of convergence and diversity of the search points. The proposed method achieves a consistently high performance through diverse search scenarios by taking advantage of the benefits of both a Pareto ranking-based method and a scalarization-based method. The performance of the proposed method was evaluated by some numerical simulations using some typical benchmark problems with different shapes of Pareto frontiers and various the number of objectives.