Evolutionary Testing (ET) is an efficient technique of automated test case generation. ET uses a kind of metaheuristic search technique, Genetic Algorithm (GA), to convert the task of test case generation into an optimal problem. The configuration strategies of GA will have notable influences upon the performance of ET. In this paper, we present a dynamic self-adaptation strategy for evolutionary structural testing. It monitors evolution process dynamically, detects the symptom of prematurity by analyzing the population, and adjusts the mutation possibility to recover the diversity of the population. The empirical results show that the strategy can greatly improve the performance of the ET in many cases. Besides, some valuable advices are provided for the configuration strategies of ET by the empirical study.