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In internal logistics, order picking is a complex task. The main reason for this is the multitude of procedures, techniques, and combinations that can be employed in the picking process. Additionally, order picking is a labor-intensive and costly area of distribution. Recent developments that lead to greater product variety, shorter delivery times, and smaller order quantities exacerbate this situation. Considerations for optimization have been around for about 30 years. However, dynamic aspects have often been neglected, leading to a focus on temporal averages. Averages are only partially realistic. Only through forecasting and the simulation-based optimization described here can the picking strategy be adjusted to real warehouses with fluctuating access numbers. This book outlines a suitable method for optimizing picking strategies for items with varying access frequencies and demonstrates the potential using real data.