The present study aimed to assess the potential of a bio-inspired algorithm, multi-objective grey wolf optimization algorithm (MOGWO), to optimize the strength properties (California bearing ratio (CBR) and unconfined compressive strength (UCS)) of an expansive subgrade soil. This optimization process involves the use of two additives, namely a bio-polymer, pregelatinized corn starch (PGCS), and a nanoparticle agro waste, rice husk ash (RHA), blended with the soil in different mix ratios determined by a 32 factorial experimental design. The CBR samples were cured for 7 days, while the UCS samples were cured for 1, 7, and 28 days. To optimize the expansive subgrade soil strength, regression models were developed using PGCS and RHA as predictors for CBR and UCS, serving as fitness functions in the slightly modified MOGWO optimization technique. Next, the optimization analysis produced non-dominated solutions. The results obtained from the laboratory experiments and optimization analysis revealed that there was significant improvement in the UCS and CBR of the soil. These improvements can be attributed to the pozzolanic reaction between the soil-RHA matrix, the formation of intercalated and exfoliated nanocomposites, and the hydrophilic interaction of PGCS. By applying the slightly modified MOGWO technique, the study achieved optimal enhancements in UCS (710.3 kN/m2) and CBR (24.2%) when the expansive subgrade soil was mixed with 0.2637% PGCS and 12.2413% RHA. The results demonstrate the potential of the MOGWO technique in improving the properties of expansive subgrade soil.