Cryptocurrencies are digital assets based on the blockchain, they use cryptography to guarantee their ownership and ensure the integrity of transactions, in addition to allowing control over the creation of additional units. Since its creation in 2009, with Bitcoin as the first cryptocurrency, the market and the number of cryptocurrencies have grown rapidly, as has the interest of those seeking high returns due to its rapid appreciation, whether these investors are individuals or financial institutions. However, this market has characteristics of high volatility and uncertainty, causing prices to vary at very high levels and also at low levels, all of which makes investment decisions in cryptocurrencies very difficult for investment managers. This article proposes a decision-making support methodology for managing investments in the cryptocurrency market, which considers a conservative investment risk profile and seeks to reduce risk and maximize investment return. The methodology aims to establish return levels and estimate the transition probabilities of returns for each level, this is done based on the historical price of cryptocurrencies and using the analysis of Markov chains, which are integrated into the multiple decision trees to identify the cryptocurrency that projects the highest return when it is sold, in one or two periods after acquisition. The results are compared with real data, and the efficiency of the methodology is verified.