Parallel machines scheduling involves the allocation of jobs to the system resources (a bank of machines in parallel). A basic model consisting of m machines and n jobs is the foundation of more complex models. Here, jobs are allocated according to resource availability following some allocation rule. In the specialised literature, minimisation of the makespan has been extensively approached and benchmarks can be easily found. This is not the case for other important objectives such as the due-date related objectives. To solve the unrestricted identical parallel machines scheduling due-date based problems, we propose two genetic algorithms (simple and multirecombinated) that incorporate problem-specific knowledge. Evidence of the improved behaviour of these genetic algorithms when compared with other genetic algorithms with and without problem-specific knowledge insertion is provided. Experiments and results are discussed.