The expenses of software testing is about 40-60% of the total cost of the software, so that reduction of test case numbers or test suite size is very much important and cannot avoid it without compromise the quality of the software. Error finding test case along with specific coverage criteria are more suitable for optimization that means the best one fit test case is selected above all and rest are ignored the number of test cases does not matter , they can be less or more either at the time of generation of test cases or after. For the reduction of test cases two options were proposed. One was at the time of generation and other was based on optimization concepts. The second case was preferred that means test case optimization after generation of the initial test case by random method. To reduce the test cases, the work was done on genetic algorithm [GA] based optimization approach.