With the ability to improve regression testing efficiency, test case prioritization has become a hot topic in software testing research. Since test case prioritization based on requirement is usually inefficient, a test case prioritization method based on discrete particle swarm optimization and test-point coverage, called Discrete Particle Swarm Optimization for Test Case Prioritization (TCP-DPSO) was proposed. Firstly, the various factors affecting prioritization were divided into two categories:Cost-Keys and Win-Keys, and then general test average yield index by normalizing was obtained. Then, particle's position and velocity were defined by use of switcher and basic switching sequence, the mutation operator was introduced by referencing mutation strategy of Genetic Algorithm (GA), and the exploration and development capabilities were adjusted by adopting variable inertia weight, which could promote sustainable evolution and approach optimization goals. The experimental results show that TCP-DPSO is similar to GA and dramatically better than random testing on optimal solution quality and it is superior to GA on success rate and average computing time, which indicates its better algorithm stability.