This thesis presents a novel use of Inductive Logic Programming (ILP) for the extraction of knowledge from a large set of job shop schedules. The purpose of this work is to apply ILP to discover the knowledge hidden in the solutions generated by a genetic algorithm performing a scheduling operation on job shop problems. The goal is to develop a rule set from this discovered knowledge, which will approximate the genetic algorithm's scheduler. Genetic algorithms are stochastic search algorithms based on the mechanics of genetics and natural selection. Because of genetic inheritance, the characteristics of the survivors after several generations should be similar. In using a genetic algorithm for job shop scheduling, the solution is an operational sequence for resource allocations. Among these optimal or near optimal solutions, similar relationships may exist between the characteristics of an operation and its sequential position. ILP, which is an intersection of machine learning and logic programming, is applied to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. These rules generate solutions which are close to the genetic algorithm's performance on an identical problem, and provide solutions that are generally superior to a simple dispatching rule for similar problems.