This work has as objective the development of an optimization methodology, using Genetic Algorithms (GAs), as evolutionary procedure coupled with the concepts of evolutionary. As case study a large scale multiphase catalytic reactor is considered. The reactor is tubular in shape and is built-up with concentric tubes using the same concept of the auto-thermal reactors, with coolant fluid flow in the external annular. The mathematical equations of the deterministic model are based on conservation principles (mass, energy and momentum) for the reactants and for the coolant fluid and validated with real operational data. The model represents the steady-state with the plug-flow assumption which is quite reasonable due to the large flow rates usually found in industrial reactors. The desired product is a specific cyclical alcohol (CA), and the minimization of the by-products is required for economical and environmental reasons. For that it is necessary to optimize some important operational parameters. This problem is of difficult solution since the reactor is a large scale system with complex behavior and conventional optimization tools as Successive Quadratic Programming tends to fail in such situation since local minima may be achieved. In this work is shown that the Genetic Algorithms technique can be useful to CA production maximization, obtaining good results with operational improvements (reduction in the catalyst rate, as well as in the undesired product rate-cycloalkane (C)). The GA parameters used for the process optimization are population size, crossover types with variation of crossover rates. The used coding was the binary form. The results are quite good, showing high performance in the CA productivity (considerable increase CA production) with changes in the operational parameters analyzed and showing that this optimization procedure is very robust and efficient. The results point out that this technique is very promising to deal with large scale system with complex behavior due to non-linearity and variable interactions. (c) 2007 Elsevier B.V. All rights reserved.