Nature-inspired metaheuristics is widely used in computer science and engineering but seems greatly underused in pharmaceutical research, clinical science research, and somewhat in statistical science as well. This class of algorithms is appealing because they are essentially assumptions free, fast and have been shown that they are capable of tackling all sorts of high dimensional complex optimization problems. We first review optimal design theory, some exemplary nature-inspired metaheuristic algorithms and show how they can be applied to (i) find efficient designs for estimating the Biologically Optimal Dose (BOD), (ii) extend Simon’s 2 stage designs for a Phase II trial with a single alternative hypothesis to one with multiple alternative hypotheses to capture the uncertainty of the efficacy of the drug more accurately, and if time permits  (iii) find a D-optimal designs for  estimating parameters in  10 interacting factors. We also indicate how metaheuristics can be applied to develop more realistic and flexible adaptive designs for early phase clinical trials.