The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($mu$GA) in the context of engineering design optimization. The basic concept behind $mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $mu$GA and SGA. Subsequently, $mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.