Evolutionary computing algorithms have proven to be effective in handling challenging optimization issues in dynamic and uncertain situations. However, due to subjective opinions and contradictory evaluation criteria, selecting the best algorithm from several options is never easy. This study suggests a novel approach for choosing the optimal evolutionary computing algorithm by using the Probabilistic Hesitant Fuzzy Sets (PHFSs). To improve discriminatory power in the context of PHFS, a novel distance measure is developed. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach is used to rank alternatives based on how close they are to an ideal solution, and the Best-Worst approach (BWM) is incorporated into the model to determine criterion weights. Ultimately, the proposed methodology is implemented on an issue concerning the selection of evolutionary computing techniques. This case study evaluates the alternatives \(A_1\) (“Differential Evolution”), \(A_2\) (“Genetic Algorithm”), and \(A_3\) (“Particle Swarm Optimization”) based on the criteria \(C_1\) (“Convergence speed”), \(C_2\) (“Computational cost”), and \(C_3\) (“Accuracy”). The findings indicate that \(A_3\) is the superior alternative. In the last, a comprehensive comparative analysis is is conducted with the existing theories, illustrating the efficacy and application of the proposed method.