While the satisfaction version of the Rotating Workforce Scheduling (RWS) problem has been extensively studied, real-life applications of the problem often require the study of an optimiza- tion variant, considering stakeholder preferences. This requires the use of multi-objective methods to solve the problem. In particular, the introduction of soft constraints often leads to conflicting objec- tives and, therefore, to a large pool of solutions of varying quality in different dimensions. Hence, it is important to be able to find suitable solutions that best meet the requirements for conflicting objectives. We propose the use of Pareto Simulated Annealing (PSA) to solve the optimization variant of RWS. This allows us to study large sets of solutions that approximate the Pareto front. Specifically, different solutions and the relations between soft constraints can be analyzed. In addition, this approach allows stakeholders to choose solutions that best meet the desired properties and comprehend which trade- offs must be made when trying to optimize conflicting objectives.