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The importance of autonomous marine vehicles is increasing in a wide range of ocean science and engineering applications. Multi-objective optimization, where trade-offs between multiple conflicting objectives are achieved (such as minimizing expected mission time, energy consumption, and environmental energy harvesting), is crucial for planning optimal routes in stochastic dynamic ocean environments. We develop a multi-objective path planner in stochastic dynamic flows by further developing and improving our recently developed end-to-end GPU-accelerated single-objective Markov Decision Process path planner. MDPs with scalarized rewards for multiple objectives are formulated and solved in idealized stochastic dynamic ocean environments with dynamic obstacles. Three simulated mission scenarios are completed to elucidate our approach and capabilities: (i) an agent moving from a start to target by minimizing travel time and net-energy consumption when harvesting solar energy in an uncertain flow; (ii) an agent moving from a start to target by minimizing travel time and-energy consumption with uncertainties in obstacle initial positions; (iii) an agent attempting to cross a shipping channel while avoiding multiple fast moving ships in an uncertain flow. Optimal operating curves are computed in a fraction of the time that would be required for existing solvers and algorithms. Crucially, our solution can serve as the benchmark for other approximate AI algorithms such as Reinforcement Learning and help improve explainability of those models.