Multi-objective Bayesian optimization (MOBO) has been shown to be a useful tool for reaction development. In this study, we focus on finding the optimal reaction conditions under heteroscedastic noise using multi-objective Euclidian expected quantile improvement (MO-E-EQI). The algorithm was first compared with some recent noisy MOBO algorithms in an in silico study with multiple noise structures and magni- tudes. Then MO-E-EQI was further implemented to guide experiments in a real case and a clear Pareto front was identified successfully.