Patent deployment has become a competition strength for companies. The intelligence property can keep the competition advantage of a company from opponents through the patent deployment which can be visualized by the patent map technique. The patent map is an important strategic tool for establishing design strategies. Our past efforts studied the display techniques in design patent map, and the comparisons of design patents in United States and Taiwan. Of types of patents, design patents occupy a unique patent field, since design patents are not as definitive as other patent fields. Therefore, the construction of design patent map is extremely difficult. Current commercial patent map systems visualize the patents according to non-populace attributes. However, such patent map systems are insufficient for providing more objective results from populace to support more powerful evidences in law courts. A key component to support the patent map system adopting the populace opinions is a fast dissimilarity visualization engine which can traslate the dissimilarity of patents from the populace opinions to a patent map. This paper presents a GA-based dissimilarity visualization engine for the above mentioned purpose. We design a set of crossover and multion operations based on the observations could generate patent maps with better quality. Our primary results reveal that the GA-based dissimilarity visualization engine indeed speeds up around 50% than the traditional method. Hence, such the engine is quite suitable for impatient users on the internet platform.