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The genetic algorithm (GA) has the issue of premature convergence, failing to make full use of feedback information and easy to fall into local optimum. At the same time, the artificial bee colony (ABC) algorithm has slow initial optimization speed and randomness local searching during the running time. This paper improves the genetic algorithm and artificial bee colony algorithm respectively. And the two improved algorithm are combined to propose an improved adaptive genetic-artificial bee colony (IAG-ABC) algorithm in order to realize the complementary advantages between the two algorithms. According to the approach level and branch distance to design fitness function and using the IAG-ABC algorithm to solve the test cases generation problem which based on path coverage. The experimental results show that the IAG-ABC algorithm has advantages about test case generation speed and path coverage rate when compare with GA and IAGA algorithm.