Goal recognition is the task of recognizing the objective of agents based on online observations of their behavior. Goal recognition design (GRD), the focus of this survey, facilitates goal recognition by the analysis and redesign of goal recognition models. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines: (1) to what extent do actions performed by an agent reveal the agent’s objective? and (2) what is the best way to modify the model so that the objective of an agent can be detected as early as possible? GRD answers these questions by offering a solution for assessing and minimizing the maximal progress of any agent before recognition is guaranteed. This approach is relevant to any domain in which efficient goal recognition is essential and in which the model can be redesigned. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. This survey presents the solutions developed for evaluation and optimization in the GRD context, a discussion on the use of GRD in a variety of real-world applications, and suggestions of possible future avenues of GRD research.