In the paint shops of the automotive supply industry, a large number of synthetic material pieces need to be painted every day to provide the large variety of items required for car manufacturing. Because of the sophisticated automated production process and the tight due dates requested by car manufacturers, finding an optimized production schedule becomes a challenging task that is at the present time performed by multiple human planners.In this paper, we formulate and solve a novel real-life paint shop scheduling problem from the automotive supply industry which introduces unique constraints and objectives that do not appear in the existing literature. Additionally, we provide a new collection of benchmark instances based on real-life planning scenarios that can be used to evaluate solution techniques for the problem.An exact approach based on constraint programming is able to provide optimal solutions for smaller instances, but many larger instances could not be solved yet. Therefore, we propose a metaheuristic method based on local search that uses novel neighborhood relations and various ways to escape local optima. Our approach is able to provide feasible solutions for all instances within reasonable running time.