The development of content in digital games, such as game worlds, quests, levels, 3D-models, and textures, is costly and time consuming. To address this, different approaches to automate the process of creating game content, often referred to as procedural content generation (PCG), has been suggested. However, PCG is a complex task and include challenges such as creating content with variation, coherent style, speed, and correctness. The research in the thesis is concerned with generating game content with the aid of game design patterns, both by establishing models and exploring different methods to generate actual game content for different games. The methods include implementations of evolutionary computation, i.e. a set of search-based approaches that searches for instances of game design patterns on different abstraction levels that make up Super Mario Bros. (SMB) levels and a learning algorithm implementation based on a model (n-grams) of patterns from the original SMB-game. The different generators were evaluated with metrics concerned with the expressive range of the generators and with user tests.