We propose a modelling framework for the optimal selection of crypto assets. We assume that crypto assets can be described according to two features: security (technological) and stability (governance). We simulate optimal selection decisions of investors, being driven by (i) their attitudes towards assets’ features, (ii) information about the adoption trends, and (iii) expected future economic benefits of adoption. Under a variety of modelling scenarios—e.g. in terms of composition of the crypto assets landscape and investors’ preferences—we are able to predict the features of the assets that will be most likely adopted, which can be mapped to macro-classes of existing crypto assets (stablecoins, crypto tokens, central bank digital currencies and cryptocurrencies).