Test case prioritization is an efficient and practical regression testing technique in the process of software evolution, and it is essential for improving early defect detection rate and reducing test cost. To solve the problem of slow convergence speed and poor stability of the traditional genetic algorithm (GA) in white box test case prioritization, we propose a test case prioritization method based on good point set GA (GGA). The GGA encodes the individuals according to the cover matrixes of program entities and uses average cover percentage of program entities as the fitness function. The GGA generates the new generation of population through random sampling selection operator and good point set crossover operator. We select six typical open source projects as benchmark programs, and take statements, branches and methods as program entities  in the experiments. Experimental results show that the GGA has faster convergence speed and better stability than the traditional GA. The GGA provides an effective method for test case prioritization in regression testing, which is helpful for finding software defects as early as possible and reducing test cost.