This paper addresses a problem regarding the joint decision of process planning and scheduling in the context of a distributed flexible job shop (DFJS). The joint decision is called integrated process planning and scheduling (IPPS). Therefore, the problem is called an IPPS/DFJS problem. This research develops a genetic algorithm (called GA_X) to solve the IPPS/DFJS problem. The GA_X algorithm is meritorious insofar as it entails the development of an incomplete modeling scheme (chromosome Φs) to represent an IPPS/DFJS solution. In chromosome Φs, only some decisions are explicitly modeled, and the remaining decisions are implicitly determined using heuristic rules that ensure load balancing among manufacturing resources. Therefore, GA_X generates load-balanced solutions and is more likely to search effectively. We optimize the genetic parameters of GA_X by conducting a full factorial experiment. Three experiments are conducted to compare GA_X with other algorithms. Experiment I involves two light-loading IPPS/DFJS instances. Experiment II involves 15 light-loading IPPS/flexible job shop (FJS) instances (degenerated cases of IPPS/DFJS problems). Experiment III involves 17 heavy-loading IPPS/DFJS instances. GA_X outperforms benchmark algorithms, and Φs (the proposed incomplete chromosome representation) has considerable merit. This finding highlights a promising direction in developing “incomplete solution representation schemes” when solving complex space-search problems with genetic or other metaheuristic algorithms.