In the printing and packaging industry, highly discrete production processes often result in serious material congestion in turnover areas. Inefficient workshop layouts hinder the smooth flow of materials, forming a key bottleneck to production efficiency. To address this, the authors propose a Systematic Layout Planning (SLP) method that integrates data-driven analysis with a genetic algorithm to optimize workshop layout for printing and packaging enterprises. The proposed method first builds a logistics intensity classification system based on traditional SLP principles, considering both logistics and non-logistics relationships between processing areas. Key parameters such as equipment capacity and material-handling frequency are incorporated to generate an initial layout. A multi-objective mathematical model is then established to minimize logistics costs and maximize non-logistics relations. A genetic algorithm is applied to iteratively optimize the layout using a fitness function that evaluates both logistics path lengths and relational proximity, with crossover and mutation strategies used to balance competing objectives. A case study of a representative printing and packaging enterprise demonstrates the effectiveness of the approach. The optimized layout reduces logistics costs by 28.15% and improves non-logistics coordination by 63%. Plant Simulation validates an 11.06% increase in production capacity and enhanced system flexibility. Compared with conventional experience-based layout methods, this approach integrates production data and evolutionary optimization, offering a data-driven solution to improve layout clarity and system adaptability in the printing and packaging sector.