Predictive methods in production planning need to be more intelligent and self-adapting in order to reduce or negate the need for human decision making and / or intervention and deliver on their potential as a key element of Industry 4.0 operations in the manufacturing enterprise. Ultimately, this will facilitate closed loop data flows and ‘lights out’ approaches to the virtual planning and validation of manufacturing systems as well as the creation and function of digital twins for the ongoing operation of the system as the product lifecycle evolves. The aim of this work is to enable a step towards a self-adapting digital toolset for manufacturing planning focusing on minimally constrained assembly line balancing. A bespoke Genetic Algorithm (GENALSAS) has been developed and demonstrated which focuses on examining the Simple Assembly Line Balancing Problem (SALBP). The approach includes the simultaneous definition of the optimum number of workstations, the optimum cycle time and the assignment of tasks to workstations. The GA has been shown to consistently deliver detailed production plans for SALBP problem forms with minimum inputs (neither the number of workstations nor the system cycle time is assumed/fixed as in previous work in the field). The work simultaneously attains better performing solutions compared with previous studies both in terms of time to converge and the quality of the solution.