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Empowerment-based workforce scheduling is a new approach that involves employees in the decision making. It enables employees to suggest their own preferences in the schedule. Employee involvement in this approach is modelled by adding to the employer's objective an additional objective that represents the overall employees' satisfaction rate. Thus, the scheduling problem becomes a biobjective optimization problem, where the task is to maximize both organizational objective(s) and employees' satisfaction level. In this paper, this problem is approached by a Pareto based local search metaheuristic, Guided Pareto Local Search (GPLS) which is an extension to the guided local search to contain multiobjective scenarios. Computational experiments show the effectiveness of GPLS, compared to a standard Pareto local search and a single-objective optimizer.