The baculovirus expression vector system (BEVS) is one of the well-known versatile platforms for the recombinant protein/vaccine production. Mathematical modeling and optimization of a baculovirus−insect cell system can have significant industrial relevance as this reduces the number of expensive experiments and time involved in the experiment-based optimization. However, modeling and control of such a nonlinear system remains challenging due to the presence of uncertainties in the model. In this context, we propose a novel computational framework combining the principles of systems biology and dynamic optimization under uncertainty for optimizing a semibatch baculovirus− insect cell system. Toward this, first, a mathematical model replicating the dynamic experimental data on cell and virus growth was identified. Next, the proposed model was used for deterministic multiobjective dynamic optimization of the control variables, substrate, and multiplicity of infection (MOI) to achieve the conflicting objectives of productivity maximization and substrate minimization, simultaneously. Finally, based on the sensitivity analysis, six of the most influential parameters depicting model uncertainties have been considered for the robust multiobjective optimal control of the system. A comprehensive comparison displays up to 114% and 76% increases in the cell densities for the deterministic and stochastic semibatch processes, respectively, compared to the batch process. Semibatch operation also favors a minimum 40% reduction in MOI required to achieve the same level of infected cell density compared to the batch operation. This study provides a generic methodology for exhibiting a proof of concept that a semibatch suspension culture considering uncertainty in model parameters can give better productivity compared to a batch suspension culture for a BEVS.