Test case prioritization (TCP) has gained significant attention due to its potential to reduce testing costs. Greedy algorithms based on various prioritization strategies are commonly used in TCP. However, most existing greedy algorithm-based TCP techniques rely on a single prioritization strategy and process all test cases simultaneously during each iteration, without considering the relationships between test cases. This results in excessive computational overhead when handling coverage information and performing prioritization, thus reducing overall efficiency. Among single-strategy approaches, the Additional strategy has been extensively studied but remains highly sensitive to random factors. When a tie occurs, test cases are typically selected at random, compromising prioritization effectiveness. To address these issues, a test case prioritization approach based on two-phase grouping (TPG-TCP) is proposed. In the first phase, coarse-grained grouping is conducted by mining hidden relationships among test cases, thus dividing them into a key group and an ordinary group. This lays the groundwork for applying diversity-based strategies in the next phase to enhance prioritization efficiency. In the second phase, fine-grained prioritization of test cases is performed. Key test cases are further subdivided based on the number of iterations. To mitigate the randomness inherent in the Additional strategy, a TP-Additional strategy based on test case potency is introduced to prioritize a portion of the key test cases. Meanwhile, a simple and efficient Total strategy is applied to prioritize the ordinary test cases and remaining key test cases. The results from the Total strategy are appended to those produced by the TP-Additional strategy. This method improves both the effectiveness and efficiency of test case prioritization. Experimental results on six datasets, compared with eight existing methods, demonstrate that the proposed method achieves average improvements of 1.29% in APFD and 9.54% in TETC.