Modern manufacturing environments strive to support a great variety of products, driven by the large customization demand, while at the same time maintain low cost and fast response to the market. To cope with the challenging manufacturing requirements, organizations rely on agile decision-making systems to maintain a smooth production function. In the paint shop environment, where hanging positions are included on a circular conveyor belt for different type of items to be transferred through the painting cabins, the provision of a near optimal scheduling solution is a rather important and complex aspect due to the diversity in colors, sizes, deadlines and more. This research paper aims to address this issue with the development of a toolbox of autonomous agents that provide near optimum solutions in the paint shop scheduling problem, by optimizing the sequence and combination of items transferred through the paint-shop conveyor. The proposed approach considers sequence-based setup time dependencies and capacity constrains for the different item types, while aiming for maximum conveyor utilization and minimum production flowtime. The smart agents’ toolbox is empowered with both model-based and data-driven optimization methods allowing it to avoid long computational delays by exploiting the different benefits of each scheduling agent. The agent interface was implemented in Java, whereas Python was used for mathematical modeling, analysis, and optimization. The research validation was performed based on real industrial data provided by a bicycle industry where the agents were tested in scheduling the painting department. Results from the performance of the different optimization methodologies within the pilot case, were used to indicate the advantages and disadvantages of each method with respect to the problem’s characteristic.