This paper proposes an approach, namely the arena’s principle (AP), to construct the Pareto optimal solutions by utilizing features of the multi-objective evolution. It is proved that the AP works correctly and its computational complexity is O(rmN) (0<1). Theoretically, when AP is compared with Deb’s algorithm and Jensen’s algorithm (their computational complexity are O(rN²) and O(Nlog^(r-1)N) respectively), AP is better than Deb’s, and is also better than Jensen’s when the objective number r is relatively large (such as r≥5). Moreover, AP performs better than the other two algorithms when m/N is relatively small (such as m/N≤50%). Experimental results indicate that AP performs better than the other two algorithms on the CPU time efficiency. In applications, AP can be integrated into any Pareto-based MOEA to improve its running efficiency.