The boom in mobile apps has changed the traditional landscape of software development by introducing new challenges due to the limited resources of mobile devices, e.g., memory, CPU, network bandwidth and battery. The energy consumption of mobile apps is nowadays a hot topic and researchers are actively investigating the role of coding practices on energy consumption. Recent studies suggest that design quality can conflict with energy consumption. Therefore, it is important to take into account energy consumption when evolving the design of a mobile app. The research community has proposed approaches to detect and remove anti-patterns (i.e., poor solutions to design and implementation problems) in software systems but, to the best of our knowledge, none of these approaches have included anti-patterns that are specific to mobile apps and–or considered the energy consumption of apps. In this paper, we fill this gap in the literature by analyzing the impact of eight type of anti-patterns on a testbed of 20 android apps extracted from F-Droid. First, we (1) analyze the impact of anti-patterns in mobile apps with respect to energy consumption; then (2) we study the impact of different types of anti-patterns on energy consumption; and (3) propose EARMO, a novel anti-pattern correction approach that accounts for energy consumption when refactoring mobile anti-patterns. Next, (4) we evaluate EARMO using three multiobjective search-based algorithms. The obtained results show that EARMO can generate refactoring recommendations in less than a minute, and remove a median of 84% of anti-patterns. Moreover, EARMO extended the battery life by up to 29 minutes when running in isolation a refactored multimedia app with default settings (no Wi-Fi, no location services, minimum screen brightness). Finally, (5) we conducted a qualitative study with developers of our studied apps, to assess the refactoring recommendations made by EARMO. Developers found 68% of refactorings suggested by EARMO to be very relevant.