Online Social Networks (OSN) have been used as platforms for many emerging applications, where trust is a critical factor for participants’ decision making. In order to evaluate the trustworthiness between two unknown participants, we need to perform trust inference along the social trust paths formed by the interactions among the intermediate participants. However, there are usually a large number of social trust paths between two participants. Thus, a challenging problem is how to effectively and efficiently find the optimal social trust path that can yield the most trustworthy evaluation result based on the requirements of participants. In this paper, the core problem of finding the optimal social trust path with multiple constraints of social contexts is modelled as the classical NP-Complete Multi-Constrained Optimal Path (MCOP) selection problem. To make this problem practically solvable, we propose an efficient and effective approximation algorithm, called T-MONTE-K, by combining Monte Carlo method and our optimised search strategies. Lastly we conduct extensive experiments based on a real-world OSN dataset and the results demonstrate that the proposed T-MONTE-K algorithm can outperform state-of-the-art MONTE K algorithm significantly.