Python package to include any combination of discrete, categorical, or ordinal parameters. We have also allowed for the definition and execution of multi-objective optimization campaigns by providing wrappers for several achievement scalarizing functions. To promote usage of these new features, we have provided access to 23 new datasets derived from chemistry and materials science literature, and 10 new analytical optimization benchmark surfaces, all of which feature either categorical parameters or multiple objectives. Several experiment planning algorithms have been updated to be compatible with the new parameter types, and 5 additional Bayesian optimization algorithms have been added. We improved the plotting and analysis capabilities of Olympus, enabling users to straightforwardly compare optimization campaigns using various metrics. Lastly, we have laid the foundations for programming-free access to the core functionality of Olympus by designing a web-based applica- tion using Streamlit. The two case studies presented, on the categorical optimization of chemical reaction conditions and the multi-objective design of redox active materials, show how Olympus allows researchers to quickly benchmark numerous optimization strategies and gain insight into their expected behaviour on realistic tasks.