Ejectors are devices that are based on the principle of momentum transfer. A primary fluid passes through a nozzle that is usually of converging-diverging cross-section so that the flow reaches supersonic velocity at the exit. Consequently, a low-pressure region is created just outside the nozzle exit. This pressure gradient, draws out the secondary fluid, into the ejector through the annular space − a phenomenon known as entrainment. This paper outlines the design and optimisation of an ejector with R141b as the working fluid. The governing equations that accurately predict the behaviour of the working fluid, have been solved using finite volume method after the discretization of the flow domain, using ANSYS Fluent. A database has been created by recording the variation of a dimensionless quantifier - entrainment ratio over several CFD simulations varying the input parameter values namely - mixing chamber radius, primary nozzle throat and exit radii. It has then been used to define a function that can precisely predict the output for an unknown set of input parameters. This has been achieved through the implementation of artificial neural networks − a surrogate modelling technique. The objective function thus obtained has been optimised with the help of genetic algorithm - a nature-inspired optimisation technique. The optimal design of the ejector for a set of operating conditions has been acquired at the output of the genetic algorithm.