The vehicle routing problem with time windows is a combinatorial optimisation problem in distribution logistics. It has been infrequently measured as a multi-objective optimisation problem for the benefit of customers. For the purposes of this research, the measurement of multi-objective vehicle routing problem with time windows will be in terms of a minimisation of the total distance travelled by all vehicles, the total number of vehicles used (management beneficial objectives) and the total gap between ready time and issuing time (customer beneficial objective). It is possible to satisfy customers by issuing the goods as close as possible to the customer ready time. This is because possibilities for going out of stock during the above said time gap (by chance of improper inventory maintenance) are reduced with a minimised total time gap, which in turn increases the sales orders for the manufacturing management. An improved genetic algorithm, called the fitness aggregated genetic algorithm, has been implemented to resolve the problem. The proposed algorithm incorporates a fitness aggregation approach and dedicated operators, such as selection based on aggregate fitness value and best cost route crossover, to resolve the multi-objective problem. The algorithm was validated on Solomon’s bi-objective benchmark models for the minimisation of the total distance travelled and total number of vehicles used, and the results formed by proposed algorithm are competitive to best known results. After validating the proposed algorithm on bi-objective models, the third objective – namely, the total time gap between ready time and issuing time – is included in the bi-objective model. The results show that the suggested algorithm creates improved customer-satisfied routes without drastically affecting the total distance travelled and total number of vehicles used.