Technical debt is the metaphor for shortcuts in software development that bring short-term benefits, but long-term consequences hinder the process of maintaining and developing software. It is important to manage these technical debt items, as not all of them need to be paid. Having a list of prioritized debts is an essential step in decision-making in the management process. This work aims at finding technical debt prioritization methods, providing a classification of them. That is, methods to identify whether and when a technical debt should be paid off. We performed a systematic mapping review to find and analyze the main papers of the area, covering the main bases. We selected 112 studies, resulting in 51 unique papers. We classified the methods in a two-level taxonomy containing 10 categories according to their different possible outcomes. In addition, we have identified three methods results: boolean, category and ordered list. Finally, we have also identified practical technical characteristics and requirements for a method to prioritize technical debt items in real projects. Although several methods have been found in literature, none of them are adaptive to the context and are language-independent, nor cover several technical debt types. Moreover, there is a clear lack of tools to use them. So, in conclusion, the research on technical debt prioritization is still wide open. From this study, a combination of the techniques used in these methods can be tested and automated to assist in the decision-making process on which debts should be paid.