Uncertainty affects many industries and sectors, including transportation and logistics, health care, production, smart cities, and finance. Simulation models are frequently employed to analyze complex systems under stochastic uncertainty. Many real-world systems are large scale and complex, requiring metaheuristic algorithms to generate high-quality solutions in reasonable computing times. However, metaheuristics are usually designed to solve optimization problems under deterministic scenarios. This chapter describes simheuristics, which merge simulation with metaheuristics to effectively tackle stochastic and complex optimization problems. It discusses the relevance of simheuristics today, reviews related research, outlines important methodological and computational aspects, and provides practical examples from various industries. Additionally, the chapter identifies open research areas and future trends in this emerging field.