With the evolution in the cancer drug development, it is of increasing interest to consider multiple strata (e.g. indications, regions or subgroups) within a single dose-finding study when identifying the maximum tolerated dose (MTD). To allow for adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum, we propose two Bayesian semi-parametric models (BSD) for dose-finding with multiple strata. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under differing prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing with existing methods, including the fully stratified, exchangeability, and exchangeability–non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD.