A hybrid approach for sequencing and scheduling is described which integrates neural networks, real-time simulation, genetic algorithms, and machine learning. This approach has been used to solve both single machine sequencing and multi machine scheduling problems. Neural networks are used to quickly evaluate and select a small set of candidate sequencing or scheduling rules from some larger set of heuristics. This evaluation is necessary to generate a ranking which specifies how each rule performs against the performance measures. Genetic algorithms are applied to this remaining set of rules to generate a single “best” schedule using simulation to capture the system dynamics. A trace-driven knowledge acquisition technique (symbolic learning) is used to generate rules to describe the knowledge contained in that schedule. The derived rules (in English-like terms) are then added to the original set of heuristics for future use. In this chapter, we describe how this integrated approach works, and provide an example.