Relying only on the cost function to calculate the optimal alignment between the model and the log may cause the alignment results to fail to accurately reflect the deviation between the actual process and the modeled process. In order to solve this problem, an optimal alignment selection strategy combining conformance log probability distribution and the cost function is proposed. Firstly, a log probability automata is constructed based on the conformance event log. Then, all possible execution sequences of the model are obtained and pre-aligned with the non-conformance log based on the cost function. Finally, all asynchronous moving left and right associative event pairs in pre-alignment are determined, and the average confidence value of the direct following dependency relationship of these event pairs is obtained according to the log probability automata. The concept of credibility value is proposed by combining the average confidence value with the pre-aligned cost value, and the alignment with the highest credibility value is considered as the optimal alignment. In the evaluation section, a large number of alignment results are evaluated, and the results show that compared with the existing two alignment methods that only based on cost function, the average alignment accuracy of the proposed method is improved by 5.55% and 10.97% respectively under various noises, and the alignment accuracy is the most stable.