We evaluate and improve an existing curvature-based region growing algorithm for colonic polyp detection for our CT colonography (CTC) computer-aided detection (CAD) system by using Pareto fronts. The performance of a polyp detection algorithm involves two conflicting objectives, minimizing both false negative (FN) and false positive (FP) detection rates. This problem does not produce a single optimal solution but a set of solutions known as a Pareto front. Any solution in a Pareto front can only outperform other solutions in one of the two competing objectives. Using evolutionary algorithms to find the Pareto fronts for multi-objective optimization problems has been common practice for years. However, they are rarely investigated in any CTC CAD system because the computation cost is inherently expensive. To circumvent this problem, we have developed a parallel program implemented on a Linux cluster environment. A data set of 56 CTC colon surfaces with 87 proven positive detections of polyps sized 4 to 60 mm is used to evaluate an existing one-step, and derive a new two-step region growing algorithm. We use a popular algorithm, the Strength Pareto Evolutionary Algorithm (SPEA2), to find the Pareto fronts. The performance differences are evaluated using a statistical approach. The new algorithm outperforms the old one in 81.6% of the sampled Pareto fronts from 20 simulations. When operated at a suitable sensitivity level such as 90.8% (79/87) or 88.5% (77/87), the FP rate is decreased by 24.4% or 45.8% respectively.