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This paper presents a design method for optimizing software-testing efficiency by identifying the most critical path clusters in a program. This is done by the application of soft computing techniques, specifically genetic algorithms. We develop a genetic algorithm that selects the software path clusters to test, which are weighted in accordance with the criticality of the path. Exhaustive software testing is rarely possible because it becomes intractable for even medium-sized software applications. Typically only parts of a program can be tested, but these parts are not necessarily the most error prone ones. Therefore, we are designing a more selective approach for testing the paths that are more critical, which results in improving the testing efficiency.