Despite their growing popularity, apps tend to contain defects which can ultimately manifest as failures (or crashes) to end-users. Different automated tools for testing Android apps have been proposed in order to improve software quality. Although Genetic Algorithms and Evolutionary Algorithms (EA) have been promising in recent years, in light of recent results, it seems they are not yet fully tailored to the problem of Android test generation. Thus, this thesis aims to design and evaluate algorithms for alleviating the burden of testing Android apps. In particular, I plan to investigate which is the best search-based algorithm for this particular problem. As the thesis advances, I expect to develop a fully open-source test case generator for Android applications that will serve as a framework for comparing different algorithms. These algorithms will be compared using statistical analysis on both open-source (i.e., from F-Droid) and commercial applications (i.e., from Google Play Store).