In order to handle costly and complex 3D metal forming optimization problems, we develop a new optimization algorithm that allows finding satisfactory solutions within less than 50 iterations (/function evaluation) in the presence of local extrema. It is based on the sequential approximation of the problem objective function by the Meshless Finite Difference Method (MFDM). This changing meta‐model allows taking into account the gradient information, if available, or not. It can be easily extended to take into account uncertainties on the optimization parameters. This new algorithm is first evaluated on analytic functions, before being applied to a 3D forging benchmark, the preform tool shape optimization that allows minimizing the potential of fold formation during the two‐stepped forging sequence.