Design has become a salient part of most of the scientific and engineering tasks, embracing a wide range of domains including real experimental settings (e.g., material discovery or drug design), simulation-based design, and hyperparameter tuning. Model-based experimental design refers to a broad class of techniques, applicable to domains that a partial knowledge about the underlying process exists. Unlike entropy- based techniques which aim to reduce the whole uncertainty in the process, the mean objective cost of uncertainty (MOCU) is a rigorous statistically-oriented experimental design framework which takes the main objective into account during the decision making. However, the lack of scalability of this framework has restricted its application to domains with very small design spaces. This paper proposes a framework using the combination of Bayesian optimization and MOCU policy, which enables experimental design to much larger design spaces and systems. The reliability, scalability and efficiency of the proposed framework are investigated through experimental design for optimal structural intervention in gene regulatory networks.