This chapter addresses two well known job shop problems, namely the classical job shop scheduling problem (JSP) and the flexible job shop scheduling problem (FJSP). Both of them belong to the category of the toughest NP-hard problems. Genetic algorithm (GA) based heuristics that have adopted Giffler and Thompson (GT) procedure, an efficient active feasible schedule generation methodology for JSP, are discussed to solve the following job shop scheduling (JSS) models: JSP for single-objective criterion (minimization of makespan time), JSP for multi-objective criterion (minimization of weighted sum of makespan time, total tardiness and total idle time of all machine) and FJSP for makespan time criterion. The chromosome representation of the GAs proposed for the JSPs is the combination of priority dispatching rules 'pdrs' (independent pdrs one each for one machine), which on decoding provides an active feasible schedule using GT procedure. The chromosome representation of the GA for FJSP consists of two strings of size equal to the total number of operations: one string for machine assignment that reduces the FJSP to a fixed route JSP and the other string is a permutation representation of priority numbers each corresponding to an operation that is used for resolving the conflict that arises while generating actives feasible schedules with GT procedure. The performance tests and validations of the proposed GAs are discussed along with future research directions. © 2009 Springer-Verlag Berlin Heidelberg.