A computational method for drop optimization for inkjet nozzle design using the Monte-Carlo method is demonstrated. To this end, a generic computational fluid dynamics (CFD) model of ink drop formation is developed as a platform for the Monte Carlo optimization. Important variables in the model are then parameterized so that they can be modified within a prescribed space. By applying the Monte-Carlo method, sensitivities of drop formation output parameters to various input parameters are studied in the context of the CFD model. Once sensitivities of drop formation to input variables are understood, the parameter space is then intelligently explored to determine a set of optimized parameters for the inkjet nozzle.