Bayesian optimisation (BO) is a sequential model-based approach utilizing surrogate models to define beliefs about the true objective function. These surrogate models are updated every time experiments are conducted. To suggest a new experiment, so-called acquisition functions that represent the utility of candidate points for the next evaluation are drawn and optimised. This also enables the BO strategies to suggest multiple points for the next evaluation sequence. Our strategies include both single-objective and multi-objective BO.