In previous work, Cobb and Zakhor developed an automated mask design algorithm using optimization to produce masks which can print at smaller feature sizes. In this work, we build upon our previous approach with special regard to computational efficiency and mask manufacturability to produce an Optical Proximity Correction (OPC) algorithm which operates orders of magnitude faster and produces simpler optimized masks. The algorithm can be used for OPC of Manhattan geometry masks for which phase assignment has been previously completed. Therefore, the OPC problem is divorced from phase-mask design and the two tasks are performed independently. Our algorithm decomposes the mask features into edges and corners which can be moved from their original placements to improve the image characteristics. The resulting optimization algorithm inherently requires computation of O((rho) o3) where (rho) o is the density of edges and corners on the mask. A major feature of the algorithm is a new fast intensity computation technique which uses lookup tables to achieve single point intensity computation on the order of O(Na (DOT) Mr) where Na is the order of approximation to the optical system and Mr is the number of rectangles in the mask region description. The single point intensity computation time is typically around 300 microsecond(s) ec for fairly complicated masks on a HP 700 series workstation. The resulting algorithm optimized a 36 X 36 micrometers 2 test mask in 6 iterations at 11 seconds per iteration pass on the same machine. The new techniques make the described algorithm viable for a production environment with k1 as low as k1 equals 0.4.