Automated generation of test data has always been a challenging problem in the area of software testing. Recently, meta-heuristic search (MHS) techniques have been proven to be a powerful tool to solve this difficulty. In the paper, we introduce an up-to-date search technique, i.e. particle swarm optimization (PSO), to settle this difficulty. After the basic idea of PSO is addressed, the overall framework of PSO-based test data generation is discussed. Here, the inputs of program under test are encoded into particles. During the search process, PSO algorithm is used to generate test inputs with the highest possible coverage rate. Once a set of test inputs is produced, test driver will seed them into program to run and collect coverage information simultaneously. Then, the value of fitness function for branch coverage can be calculated based on such information, which can direct the algorithm optimization in next iteration. In order to validate our method, five real-world programs are used for experimental analysis. The results show that PSO-based method outperforms other algorithms such as GA both in the coverage effect of test data and the convergence speed.