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In the process of software development and maintenance, software debugging is the most complicated and the most expensive part. During the period of traditional software debugging, programmers have to locate mistakes by browsing codes, this is a time-consuming and laborious work. There has been a great need for fault localization techniques that can help guide programmers to the locations of faults. In recent years, automated software fault localization technology has attracted many scholars’ attention, various approaches have been proposed. In this paper, a technique named EGA-BPN is proposed which can propose suspicious locations for fault localization automatically without requiring any prior information of program structure or semantics. EGA-BPN is a software fault localization method based on enhanced Genetic Algorithm-Back Propagation neural network. Firstly, through processing running traces of the program, covering information of test cases are converted as the training samples of neural network; secondly, the data are input into neural network in training orderly, the initial weights of neural network are computed by GA, then test matrix is calculated by the neural network to count the suspiciousness of each statement, and using orthogonal experimental design to adjust the parameters of neural networks; finally, the fault is located at the statements with higher suspicious value. Through experiment on the proposed method and GA-BPN and BPN were compared, the results show that the enhanced GA-BP neural network-based fault localization technology has certain validity.