Scheduling problems are ubiquitous in various domains, requiring efficient allocation of resources and coordination of tasks to optimize performance and meet desired objectives. Traditional approaches to scheduling often face challenges when dealing with complex and dynamic environments. In recent years, multi-agent systems have emerged as a promising paradigm for addressing scheduling problems. This paper presents a comprehensive survey of learning in multi-agent systems to solve scheduling problems. One hundred twenty-one articles were retrieved from the Scopus and WOS databases, 55 of which were reviewed and analyzed in depth. The results indicate that Reinforcement Learning (RL) is the learning model used in the reviewed articles. Our analysis also identified a tendency to combine two or more RL algorithms to be applied. Furthermore, most of the articles focus on solving dynamic scheduling problems in the manufacturing, wireless and communication network industries.