Software testing is one of the most important processes in the software development life cycle. As software evolves, previous test cases need to be re-executed to make sure that there is no new bugs introduced and nothing is broken in the existing behaviours. However, re-execution of all test cases could be expensive. That is why, test case prioritization method can be used to detect faults earlier by prioritizing the test cases which could have the higher possibility than others to find faults. Studying different approaches, implementing different techniques or putting these techniques to test on different programs could make it easier to answer which technique should be used for which kind of programs or faults. We address this issue, focusing on selecting different test case prioritization approaches and calculating the average fault detection ratios of prioritized test suites. As a novelty, we propose to perform an optimization algorithm on one of the approaches called `Clustering` to increase its efficiency. To do that, our main objective is determined as maximizing the distance between each clusters by using the coverage information. The distance is measured as the difference of covered functions of test cases in a test suite. In the end, this study will give a hint about selection of test case prioritization technique to be used by checking the empirical results of the experiments.