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Metaheuristics embrace a powerful family of optimization methods. These algorithms are created with the intent of mimicking some types of natural phenomena (such as the principles of physics, the theory of evolution, the communal behavior of groups of animals, or human behavior and style) and employing them to tackle difficult problems. Since the first metaheuristic was proposed, significant progress has been made, and countless new algorithms are constantly being proposed on a daily basis. On the other hand, the Activity Chain Optimization Problem is a combinatorial problem based on the Traveling Salesman Problem, which aims to optimize the daily activity schedules of individuals. Due to the complexity of solving these complex problems, metaheuristics are required as primary methods. Thus, this paper investigates the contribution of metaheuristics to solving the Activity Chain Optimization problem. We mapped descriptive and assessment features for 63 metaheuristics based on a metaheuristic classification. The findings are examined to reveal the usage tendencies of the algorithms, identifying the most prevalent and those that have potential for future research. Additionally, we open a discussion regarding a number of unexplored research gaps and prospects in this appealing scientific field.