In this work a model-based optimization study of fed-batch BHK-21 cultures expressing the human fusion glycoprotein IgG1-IL2 was performed. It was concluded that due to the complexity of the BHK metabolism it is rather difficult to develop a kinetic model with sufficient accuracy for optimization studies. Many kinetic expressions and a large number of parameters are involved resulting in a complex identification problem. For this reason, an alternative more cost-effective methodology based on hybrid grey-box models was adopted. Several model structures combining the a priori reliable first principles knowledge with black-box models were investigated using data from batch and fed-batch experiments. It has been reported in previous studies that the BHK metabolism exhibits modulation particularities when compared to other mammalian cell lines. It was concluded that these mechanisms were effectively captured by the hybrid model, this being of crucial importance for the successful optimization of the process operation. A method was proposed to monitor the risk of hybrid model unreliability and to constraint the optimization results to acceptable risk levels. From the optimization study it was concluded that the process productivity may be considerably increased if the glutamine and glucose concentrations are maintained at low levels during the growth phase and then glutamine feeding is increased.