In this study, a hybridised bi-objective optimisation technique was proposed to optimise process parameters (additives) in pavement engineering. The two process parameters, rice husk ash (RHA) and quarry dust (QD), which were used for the treatment of the soft subgrade soil, were set as input parameters in the development of two regression functions with the RSM optimisation technique. Next, the developed regression functions were utilised as fitness functions in the MOGOA and Pareto optimal solutions that represent different optimum combinations of additives. Subsequently, a selected optimum combination of additives (14.5% RHA and 14.4% QD) was used to validate the proposed hybridised RSM-MOGOA technique. The predicted values by the RSM-MOGOA technique for the 28-day unconfined compressive strength (UCS) and the California bearing ratio (CBR) of the soft subgrade were 729.9 kN/m2 and 50.4%, respectively while those obtained from confirmatory experiment were 736.50 kN/m2 and 54.4%, respectively for the UCS and CBR.