We report on the possibilities of a new method development (MD) algorithm that searches the chromatographic parameter space by systematically shifting and stretching the elution window over different parts of the time-axis. In this way, the search automatically focuses on the most promising areas of the solution space. Since only the retention properties of the first and last eluting compounds of the sample need to be (approximately) known, the algorithm can be directly applied to samples with unknown composition, and the proposed solutions are not sensitive to any modeling errors. The search efficiency of the algorithm has been evaluated on an extensive set of random-generated in silico samples covering a broad range of different retention properties. Compared to a pure grid-based search, the algorithm could reduce the number of missed components by 50% and more. The algorithm has also been applied to solve three different real-world separation problems from the pharmaceutical industry. All problems could be successfully solved in a very short time (order of 12h of instrument time).