Abstract Intensity modulated particle therapy (IMPT) with carbon ions can generate highly conformal treatment plans; however, IMPT is limited in robustness against range and positioning uncertainty. This is particularly true for moving targets, even though all motion states of a 4DCT are considered in 4D-IMPT. Here, we expand 4D-IMPT to include robust non-linear RBE-weighted optimization to explore its potential in improving plan robustness and sparing critical organs. In this study, robust 4D-optimization—based on worst-case optimization on 9 scenarios—was compared to conventional 4D-optimization with PTV margins using 4D dose calculation and robustness analysis for 21 uncertainty scenarios. Slice-by-slice rescanning was used for motion mitigation. Both 4D-optimization strategies were tested on a cohort of 8 multi-lesion lung cancer patients with the goal of prioritizing OAR sparing in a hypofractionated treatment plan. Planning objectives were to keep the OAR volume doses below corresponding limits while simultaneously achieve CTV coverage with D95% ≥ 95 %. For the conventional plans, average D95% was at 98.7% which fulfilled the target objective in 83.2% of scenarios. For the robust plans, average D95% was reduced to 97.6% which still fulfilled the target objective in 80.7% of cases, but led to significantly improved overall OAR sparing: Volume doses were below the limits in 96.2% of cases for the conventional and 99.5% for the robust plans. When considering the particularly critical smaller airways only, fulfillment rates could be increased from 76.2% to 96% for the robust plans. This study has shown that plan robustness of 4D-IMPT could be improved by using robust 4D-optimization, offering greater control over uncertainties in the actual delivered dose. In some cases, this required sacrificing target coverage for the benefit of better OAR sparing.