The objective of this study was to develop an efficient sampling strategy to predict epigallocatechin gallate (EGCG) pharmacokinetics after green tea administration. Ten healthy subjects received a single 800-mg oral dose of EGCG administered as Polyphenon E under both fasting and fed conditions. Plasma samples were serially collected over 24 hours and EGCG concentrations were determined. A one-compartment model with a lag time for absorption best fit the concentration-time data. Maximum A Posteriori Bayesian (MAPB) priors were developed by simultaneously fitting pharmacokinetic parameters from both study phases. The D-optimal sampling designs were determined and Monte Carlo simulations were performed. The original model with the estimators was used to fit the simulated data with the optimized sampling schemes. Two and three optimal sampling strategies (OSS-2 and OSS-3, respectively) were developed. The median two sampling times for OSS-2 were 1.3 and 6.9 hours (fasting conditions) and 3.4 and 8.7 hours (fed conditions). The median three sampling times for OSS-3 were 0.7, 1.4, and 7.0 hours (fasting conditions) and 1.4, 3.6, and 8.7 hours (fed conditions). The predictive power of OSS-3 was greater than that of OSS-2, under both fasted and fed conditions, and both strategies had greater predictive performance under fasting conditions. The sampling schemes were accurate and precise in predicting EGCG oral clearance (or area under the curve with known doses), and hence exposure, under both fasting and fed conditions. The increased predictive performance for estimating pharmacokinetic parameters under fasting conditions appeared to be the result of a decreased variability in absorption.