Constraint Programming (CP) is a powerful optimization method that provides optimality guarantees, but due to its exact nature, its scalability to large instances is often limited. To address this, we propose a hybrid approach that combines a CP solver with heuristic methods, directly and asynchronously exchanging solutions and objective values during runtime. The hybrid parallel configuration yields faster convergence to good solutions across various problem domains than the solver alone, while retaining the ability to guarantee optimality, leveraging the complementary nature of the CP solver and heuristics. The efficiency of this approach is evaluated on three well-known scheduling problems and three well-known routing problems. Noticeable improvements are observed for the Flow-shop Scheduling Problem (FSSP), the Traveling Salesman Problem (TSP), and the Vehicle Routing Problem with Time Windows (VRP-TW). Even for problems where improvements are marginal, the portfolio of methods increases the robustness of the approach.