This paper presents the method of variable-feedrate intelligent segmentation as an enhanced approach to feedrate optimization in micromilling that overcomes detrimental scale effects in the process and leads to improved stability and decreased machining time. Due to the high tool-size to feature-size ratio present in micromilling, the maximum allowable feedrate is limited by the sampling rate of the real-time trajectory generation and motion control system. The variable-feedrate intelligent segmentation method is proposed to compensate for the feedrate limitation by intelligent selection of the interpolation technique applied to segments along the tool path in order to reduce the trajectory generation computation time and enable increased sampling frequency. The increased sampling frequency allows higher maximum feedrates providing for increased productivity and improved process stability. The performance of the novel intelligent segmentation approach was benchmarked against recent non-rational B-splines (NURBS) feedrate optimization techniques. Results from the numerical evaluation of the intelligent segmentation technique have demonstrated significant reductions in machining time, with a maximum reduction of over 50% recorded. Furthermore, the results from the study demonstrate the advantages of the intelligent segmentation method in enhancing process stability and maintaining, or marginally decreasing, process error. The variable feedrate intelligent segmentation method developed in this study provides, therefore, an enhanced methodology for path planning in high-speed, high-precision micromilling operations.