The predictive capability of any mathematical model is intertwined with the quality of experimental
data collected for its calibration. Model-based design of experiments helps compute maximally
informative campaigns for model calibration. But in early stages of model development it is crucial to
account for model uncertainties to mitigate the risk of uninformative or infeasible experiments. This
article presents a new method to design optimal experimental campaigns subject to hard constraints
under uncertainty, alongside a tractable computational framework. This computational framework
involves two stages, whereby the feasible experimental space is sampled using a probabilistic approach
in the first stage, and a continuous-effort optimal experiment design is determined by searching over
the sampled feasible space in the second stage. The tractability of this methodology is demonstrated
on a case study involving the exothermic esterification of priopionic anhydride with significant risk of
thermal runaway during experimentation. An implementation is made freely available based on the
Python packages DEUS and Pydex.