In this work it is proposed a suitable hybrid optimization algorithm built up with the association of global and local optimization methods. The adopted computer assisted approach is driven by the need for a global optimization method characterized by efficiency in terms of reduced computational time and efforts whereas being robust. The basic idea is to join the fast convergence properties of gradient-based optimization methods with the wide exploration ability of population-based ones, which makes the developed algorithm a useful tool in real-time applications. Since unavoidable disturbances are present during process operation, efficient optimization algorithms must be available to deal in an on-line fashion with high dimensional and non-linear processes. In the developed code, a Genetic Algorithm (GA) is designed to provide an estimate of the global optimum. Then, a local method of search (the Sequential Quadratic Programming SQP) is used to improve this candidate solution. As case study, the optimization of a three-phase catalytic slurry hydrogenation reactor is considered. The optimization algorithm determines, in real time, the optimal operating condition, defined in terms of maximization of profit. This condition should then be used in an advanced control layer. The results of the hybrid approach are compared with those obtained only considering the micro-GA. The latter approach was able to, alone, solve the optimization problem, but using a large number of generations and, consequently, with higher computational time. The advantage of the hybrid algorithm are that fewer number of generations is employed prior to the SQP utilization. Thus, the new GA-SQP code was able to determine the final solution considerably faster than the isolated GA, reducing the number of functions evaluations for solutions when compared to the number required for the GA to stop the evolution. The hybrid algorithm drives to feasible solution translated into higher profits at reasonable computational costs, being identified as a robust optimization code, useful in real time optimization applications.