Background Diagnosis of congenital long‐QT syndrome (LQTS) is complicated by phenotypic ambiguity, with a frequent normal‐to‐borderline resting QT interval. A 3‐step algorithm based on exercise response of the corrected QT interval (QTc) was previously developed to diagnose patients with LQTS and predict subtype. This study evaluated the 3‐step algorithm in a population that is more representative of the general population with LQTS with milder phenotypes and establishes sex‐specific cutoffs beyond the resting QTc. Methods and Results We identified 208 LQTS likely pathogenic or pathogenic KCNQ1 or KCNH2 variant carriers in the Canadian NLQTS (National Long‐QT Syndrome) Registry and 215 unaffected controls from the HiRO (Hearts in Rhythm Organization) Registry. Exercise treadmill tests were analyzed across the 5 stages of the Bruce protocol. The predictive value of exercise ECG characteristics was analyzed using receiver operating characteristic curve analysis to identify optimal cutoff values. A total of 78% of male carriers and 74% of female carriers had a resting QTc value in the normal‐to‐borderline range. The 4‐minute recovery QTc demonstrated the best predictive value for carrier status in both sexes, with better LQTS ascertainment in female patients (area under the curve, 0.90 versus 0.82), with greater sensitivity and specificity. The optimal cutoff value for the 4‐minute recovery period was 440 milliseconds for male patients and 450 milliseconds for female patients. The 1‐minute recovery QTc had the best predictive value in female patients for differentiating LQTS1 versus LQTS2 (area under the curve, 0.82), and the peak exercise QTc had a marginally better predictive value in male patients for subtype with (area under the curve, 0.71). The optimal cutoff value for the 1‐minute recovery period was 435 milliseconds for male patients and 455 milliseconds for femal patients. Conclusions The 3‐step QT exercise algorithm is a valid tool for the diagnosis of LQTS in a general population with more frequent ambiguity in phenotype. The algorithm is a simple and reliable method for the identification and prediction of the 2 major genotypes of LQTS.