Munir et al. 8 investigated the production of biodiesel from Capparis spinosa L. seed oil.The properties of the biodiesels depend on the properties of feedstock 9 . To use biodiesel as a fuel, the properties of the biodiesel should comply with biodiesel standards 10 . Biodiesel standards specify the limits for the properties such as density, kinematic viscosity, acid value, iodine value, induction period, cetane number, copper corrosion, calorific value, flash point, cloud point, pour point, cold filter plugging point CFPP , etc. 10 . The American standard ASTM D6751 and European standard EN 14214 are the commonly used biodiesel specifications 10 . Selected properties of different biodiesels from literature are given in Table 1 2 . From Table 1, it is seen that the many biodiesels do not have properties within the limits specified by the standards 10 .One of the approaches to overcome the biodiesel quality issue is to blend different biodiesels such that the resulting biodiesel blend meets the specifications. Moser 11 had adopted this approach and studied the fuel property enhancement of biodiesels through the complementary blending of field pennycress biodiesel, and meadowfoam Abstract: Biodiesel is a viable alternative to petroleum diesel. The properties of the biodiesel depend on the feedstock used to produce it. A significant difference in properties exists between different biodiesels. Therefore, standards for biodiesel fuel had been developed considering many factors such as safe handling, corrosion, ignition quality, stability, cold flow property, and performance. For using biodiesel as fuel, the properties of the biodiesel should be within the limits specified in the standard. Unfortunately, biodiesel produced from many feedstocks does not comply with the specifications for all the properties. To utilize biodiesel with poor quality, biodiesels can be blended so that the properties of the blend comply with the specifications. Determining the optimal biodiesel blend ratio experimentally requires a lot of effort particularly when the number of parameters to be optimized is more and when the number of constituent biodiesels in the blend is more. In this work, the application of non-dominated sorting genetic algorithm to predict optimal blends for different scenarios is demonstrated.