The main objective of this study is to develop an offline tuning of the operating input parameters for a sludge drying operation, by using multiobjective optimization techniques combined with a predictive control method. The manipulated variables concerned are the temperature and the relative humidity of the drying air (T-air, R-Hair). The optimal time for the reversal operation of the product is also investigated. The optimization procedure is coupled to a one-dimensional numerical model that allows the simulation of moisture content and temperature field evolutions in the product during the drying step. A genetic algorithm is used to identify the two manipulated variables, at each step time, by minimizing simultaneously three objective functions over a finite horizon. These objective functions are linked to penalties concerning the heating and dehumidifying of the outside air used for the drying stage and to a global moisture content gap relative to a drying target. First, the heat and mass transfer model is validated for the drying step of a plate sample of sludge, with a reversal operation. Afterwards, the optimization procedure is carried out, and the results are discussed in terms of an energetic analysis. (C) 2015 Elsevier Ltd. All rights reserved.