This paper presents a Bayesian methodology for computer-aided experimental design for hydrogen peroxide formulations. Hydrogen peroxide is one of the oldest known active antimicrobial chemicals and is used in many cleaning/disinfecting formulations. It is favourable as an active antimicrobial in that it degrades only to water and oxygen, and does not contaminate the environment. However hydrogen peroxide is difficult to stabilise, and disinfecting products based on it soon lose their antimicrobial activity. Moreover, regulatory agencies such as U.S. Environmental Protection Agency (EPA) and Health Canada require that disinfecting products do not lose more than 5-10% of their active concentration throughout their shelf life. Therefore, it is very important while formulating hydrogen peroxide-based products to test for their stability. An effective way to improve hydrogen peroxide stability in a solution is to use stabilisers. It is desired to use these chemicals in as low concentrations as possible for environmental and economic considerations. On the other hand, due to tight market competition, the new products need to be formulated as quickly as possible, and therefore there is limited time to ensure product stability. In this paper, prior information has been used in the form of a model, based on historical experiments. A Bayesian D-optimality criterion is used to design a few additional experiments so that the resulting model can have an acceptable prediction power. It is shown that a design which uses the Bayesian D-optimality criterion taking advantage of prior information can be more efficient than even a resolution IV fractional factorial design in the sense that using fewer trials gives a model with equivalent prediction capability. This can be critical where experiments are expensive to perform.