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Abstract Background It has been estimated that automated peritoneal dialysis (APD) is currently the fastest growing renal replacement therapy in the world. However, in light of the growing number of diabetic patients on peritoneal dialysis (PD), the unwanted glucose absorption during APD remains problematic. Recent results, using an extended 3-pore model of APD, indicated that large reductions in glucose absorption are possible by using optimized bi-modal treatment regimens, having “UF cycles” using a higher glucose concentration, and “Clearance cycles” using a low concentration or, preferentially, no glucose. The present study is designed to test the theoretical prediction of a lower glucose absorption using these novel regimes. Methods This study is a randomized single-center, open-label, prospective study. Prevalent PD patients between 18 and 75 years old without known catheter problems or recent peritonitis are eligible for inclusion. Patients are allocated to a first treatment session of either standard APD (6 × 2 L 1.36% over 9 h) or optimized APD (7 × 2 L 2.27% + 5 × 2 L 0.1% over 8 h). A second treatment session using the other treatment will be performed in a crossover fashion. Samples of the dialysis fluid will be taken before and after the treatment, and the volume of the dialysate before and after the treatment will be carefully assessed. The primary endpoint is difference in glucose absorption between the optimized and standard treatment. Secondary endpoints are ultrafiltration, sodium removal, Kt/V urea, and Kt/V Creatinine. The study will be closed when a total of 20 patients have successfully completed the interventions or terminated according to interim analysis. A Monte Carlo power analysis shows that the study has 80% power to detect a difference of 10 g (in line with that of theoretical results) in glucose absorption between the two treatments in 10 patients. Discussion The present study is the first clinical investigation of optimized bi-modal treatments proposed by recent theoretical studies. Trial registration ClinicalTrials.gov identifier: NCT04017572 . Registration date: July 12, 2019, retrospectively registered.