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Software testing is a very expensive and time consuming process. Test methods which generate test data based on the program's internal structure are intensively used. This paper presents a comparison between three important Evolutionary Algorithms used for automatic test data generation, a technique that forces the execution of a desired path of the program called target path. Two new approaches, based on Particle Swarm Optimization and Simulated Annealing algorithms, used in conjunction with the approximation level and branch distance metrics, are compared with Genetic Algorithms for generating test data. The results obtained based on the proposed approaches suggest that evolutionary testing strategies are very well suited to generate test data which cover a target path inside a software program.