This thesis work addresses the short-term scheduling problem in a Permutation Flow Shop environment, considering stochastic processing times, through a multiobjective approach that generates Pareto Frontiers for Mean and Standard Deviation of Total Tardiness. This is accomplished by designing a simheuristic that combines the Tabu Search Metaheuristic and the PAES algorithm with a Monte Carlo simulation process for calculating both objectives. The design process required the analysis of the deterministic approach through linear programming and Metaheuristics considering a single objective. Then, the stochastic approach was addressed through the proposed method. The effectiveness of the simheuristic designed was evaluated for two different probability distributions of the stochastic parameter –lognormal distribution a uniform distribution- and three coefficients of variation -0.3, 0.4, and 0.5-. The results were compared with the simulated solutions given by the Tabu Search algorithm programed for the deterministic case. An analysis of variance was done, with 180 benchmark instances, for evaluating the proposed method, considering the three factors mentioned before, for both Mean and Standard Deviation of the Total Tardiness. Instances sizes are (in jobs x machines) 50x10, 50x30, 150x10 and 150x30 and were generated with three different parameters of tardiness factor and due date range. Results show that, in 176 of the 180 instances, the simheuristic presented a significantly better result than that generated by deterministic approach, for both objectives analyzed.