The Multi-Objective Shortest Path (MOSP) problem generalizes the classic shortest path problem by simultaneously optimizing multiple, often conflicting, cost functions. Recent advances in MOSP have yielded algorithms that employ sophisticated heuristic-based techniques and dimensionality reduction to expedite search. However, most existing methods rely on strictly sequential frameworks, leaving parallelized approaches relatively underexplored - especially for high-dimensional objectives. In this paper, we introduce SOPMOA* (Shared-Open Parallelized Multi-Objective A*), an algorithm that addresses this gap by enabling any number of concurrent sub-searchers to cooperate via a shared-memory priority queue. Each sub-searcher independently processes labels, performs dominance checks against locally stored partial Pareto fronts, and contributes to a global frontier of non-dominated solutions. We propose mechanisms for safe and efficient updates to shared data structures, ensuring correctness without excessive locking overhead. Empirical evaluations on benchmark multi-objective road networks demonstrate that SOPMOA* scales favorably with increasing parallelism and consistently outperforms state-of-the-art algorithms such as EMOA*, LTMOA*, and NWMOA* in both speed and robustness. These results underscore the substantial potential of shared-memory parallelization in tackling challenging multi-objective pathfinding tasks.