Software testing is a laborious job, and accounts for a large portion of software development expenses. Search-based automatic test case generation is an area of research that attempts to remedy this by discovering algorithms suited for generating test cases automatically. In this field, DynaMOSA is a state-of-the-art evolutionary algorithm, which reduces the problem of test case generation to a multi-objective optimization problem, and uses domain knowledge to generate solutions efficiently. In this paper, we adapt Pareto Corner Search Evolutionary Algorithm (PCSEA) for test case generation. Furthermore, we integrate DynaMOSA heuristics into PCSEA, and create a novel algorithm, DynaMOSAPCSEA. We evaluate the test case generation efficacy of PCSEA, DynaMOSA, and DynaMOSAPCSEA by using the JavaScript benchmark provided by SynTest Framework. The results indicate that PCSEA is a feasible algorithm for test case generation, however DynaMOSA heuristics improve its performance only minimally.