<p>Test case prioritization is aimed at detecting the fault as early as possible which is largely employed in program regression testing. The previous test case prioritization schemes are affected by the lower capability to predict the faults and incur more computational overhead. To bridge this gap, a novel framework Jaya Archimedes Optimization Algorithm (Jaya AOA) is developed for test case prioritization. Two phases involved in this model are test case generation and test case prioritization. Initially, the software program is applied to the test case generation. After that, the generated test cases are applied to the test case prioritization, which is prioritized by the Jaya AOA model. Here, Jaya AOA is the combination of the Jaya Algorithm (Jaya) and Archimedes Optimization Algorithm (AOA). Two objectives are considered during prioritization are Average Percentage of Fault Detected (APFD), and the Average Percentage of Branch Coverage (APBC). APFD is the weighted average of the percentage of faults identified throughout the duration of a test suite. APBC is defined as the weighted average of the percentage of branches covered over the lifespan of a test suite. The evaluation metrics employed in this scheme Average Percentage of Fault Detected (APFD), Average Percentage of Branch Coverage (APBC), Average percentage of decision covered (APDC), Percentage of Remaining Fault Detected (PRFD), and Rate of Remaining Fault detection (RRTF) acquired maximum value of 0.934, 0.968 0.932, 0.941, and 0.943 in space. The source code of the devised model is available at <a href="https://github.com/SSugave/Jaya-AOA.git">https://github.com/SSugave/Jaya-AOA.git</a>.</p>