Objective: The aim of the study was to evaluate the performance of the first-trimester Fetal Medicine Foundation (FMF) screening algorithm, including maternal characteristics and medical history, blood pressure, pregnancy-associated plasma protein A and placenta growth factor, crown rump length, and uterine artery pulsatility index, for the prediction of preeclampsia in a high-risk population in the Netherlands. Methods: This is a prospective cohort including nulliparous women and women with preeclampsia or intrauterine growth restriction in previous pregnancy. We screened patients at 11–14 weeks of gestation to calculate the risk for preeclampsia. The primary outcome was preeclampsia and gestational age at delivery. Performance of the model was evaluated by area under the receiver operating characteristic (ROC) curves (AUCs) and calibration graphs; based on the ROC curves, optimal predicted risk cutoff values for our study population were defined. Results: We analyzed 362 women, of whom 22 (6%) developed preeclampsia. The algorithm showed fair discriminative performance for preeclampsia <34 weeks (AUC 0.81; 95% CI 0.65–0.96) and moderate discriminative performance for both preeclampsia <37 weeks (AUC 0.71; 95% CI 0.51–0.90) and <42 weeks (AUC 0.71; 95% CI 0.61–0.81). Optimal cutoffs based on our study population for preeclampsia <34, <37, and <42 weeks were 1:250, 1:64, and 1:22, respectively. Calibration was poor. Conclusions: Performance of the FMF preeclampsia algorithm was satisfactory to predict early and preterm preeclampsia and less satisfactory for term preeclampsia in a high-risk population. However, by addressing some of the limitations of the present study, the performance can potentially improve. This is essential before implementation is considered.