Speculative multithreading (SpMT) technology is an effective mechanism for automatic parallelization of irregular programs. However, just generating speculative threads based on the control flow graph which only contains branch probability information is not enough. It is inevitable that there may be excessive constraints resulting from control dependence and data dependence in practice. In the traditional thread partitioning algorithms, a key problem is that these existing evaluation methods do not integrate the data dependence with the control speculation in order to obtain the maximum benefit. In order to solve the inefficiency of evaluation for control dependence and data dependence, this paper proposes a path optimization based thread partitioning algorithm. In this algorithm, by introducing pre-computation technique and discussing the trade-offs between different speculative paths the study establishes an evaluation model to evaluate the control dependence and data dependence. Meanwhile, in order to reduce the side-effect of workload imbalance, some heuristics rules are designed. The experimental results show that there are interesting trade-offs between different speculative paths and it is possible to achieve a better performance. On average, the study achieved an average speedup of 1.83 on Olden benchmark suits.