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The most expensive and the time consuming step of software development life cycle is its testing. There are several research proposed to develop a low cost, scalable and effective method for soft- ware testing. With the help of the techniques of automatic generation of test cases one can easily and very efficiently find an optimal set of cases that allow an appropriateness criterion to be fulfilled, which helps in reducing the cost of software testing and resulting in more efficient software testing. In this paper we are trying to discuss a new technique for automated test case generation using teaching learning based optimization. This technique exte-nds the random testing by the use of leaching learning based optimization where the fitness function is based on the branch coverage.