Abstract OptQuest developed by OptTek Systems, Inc. is a black-box solver that has been in development for over 30 years and has hundreds of thousands of licensed users across all industries. The OptQuest optimization engine implements black-box optimization through a metaheuristic framework that orchestrates multiple lower-level heuristics without relying on explicit problem formulations, gradients, or assumptions about convexity or smoothness. Metaheuristics operate as high-level, problem-independent strategies that dynamically combine and guide heuristics to balance exploration and exploitation in large search spaces: scatter search generates new candidates from combinations of elite solutions, tailored to variable types including continuous, integer, binary, discrete, categorical, and permutation; tabu search employs short- and long-term memory structures to avoid revisiting recent or poor regions and escape local optima; population-based approaches such as genetic algorithms evolve solution sets via fitness-based selection, recombination, and mutation; particle swarm optimization updates solution trajectories using velocity vectors influenced by personal and global bests with constriction for convergence; and surrogate models are constructed from evaluated points to predict and guide toward promising areas.