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Concept generation in conceptual design is a process of combinatorial optimization in nature. In this paper, Genetic Algorithm (GA) is utilized as a feasible tool to solve the problem of combinatorial optimization in concept generation, in which an improved encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. In this work, concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved.