The increasing complexities of wellbore geometry imply an increasing well cost. It has become more important than ever to achieve an increased rate o f penetration (ROP) and, thus, reduced cost per foot. To achieve maximum ROP, an optimization o f drilling param eters is required as the well is drilled. While there are different optimization techniques, there is no acceptable universal mathematical model that achieves maximum ROP accu rately. Usually, conventional mathematical optimization techniques fail to accurately predict optimal parameters owing to the complex nature o f downhole conditions. To account fo r these uncertainties, evolutionary-based algorithms can be used instead o f mathematical optimizations. To arrive at the optimum drilling parameters efficiently and quickly, the metaheuristic evolutionary algorithm, called the ''shuffled frog leaping algo rithm, (SFLA) is used in this paper. It is a type o f rising swarm-intelligence optimizer that can optimize additional objectives, such as minimizing hydromechanical specific energy. In this paper, realtime gamma ray data are used to compute values o f rock strength and bit-tooth wear. Variables used are weight on bit (WOB), bit rotation (N), and flow rate (Q). Each variable represents a frog. The value o f each frog is derived based on the ROP models used individually or simultaneously through iteration. This optimizer lets each frog (WOB, N, and Q) jump to the best value (ROP) automatically, thus arriving at the near optimal solution. The method is also efficient in computing opti mum drilling parameters for different formations in real time. The paper presents field examples to predict and estimate the parameters and compares them to the actual real time data.