Tree-based search algorithms applied to combinatorial optimization problems are highly irregular and timeconsuming when solving instances of NP-Hard problems. Due to their parallel nature, algorithms for this class of complexity have been revisited for different architectures over the years. However, parallelization efforts have always been guided by the performance objective setting aside productivity. Using Chapel's high productivity for the design and implementation of distributed tree search algorithms keeps the programmer from lower-level details, such as communication and load balancing. However, the parameterization of such parallel applications is complex, consisting of several parameters, even if a high-productivity language is used in their conception. This work presents a local search for automatic parameterization of ChapelBB, a distributed tree search application for solving combinatorial optimization problems written in Chapel. The main objective of the proposed heuristic is to overcome the limitation of manual parameterization, which covers a limited feasible space. The reported results show that the heuristic-based parameterization increases up to 30% the performance of ChapelBB on 2048 cores (4096 threads) solving the N-Queens problem and up to 31% solving instances of the Flow-shop scheduling problem to the optimality.