Optimisation, which is a method to obtain optimal or near-optimal values of objective functions, has been widely used to make a decision in many problem domains, such as engineering, chemical, business, etc. This research is aimed to build an R package that implements 11 methods based on meta-heuristics methods that are inspired by natural phenomena and animal behaviours. Here, R programming language is considered since it is a popular programming language for data science. In this version of the package, 11 meta-heuristic algorithms are implemented, namely particle swarm optimisation (PSO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA), firefly algorithm (FFA), genetic algorithm (GA), grasshopper optimisation algorithm (GOA), moth flame optimizer (MFO), sine cosine algorithm (SCA), whale optimisation algorithm (WOA), and harmony search (HS). The methods have proven to be reliable and stable. To validate the package, the study presents 13 benchmarking functions in our experiments such as sphere model, Schwefel's Problem 2.22, Generalised Rosenbrock's Function and Step Function. Based on the experiments, package metaheuristicOpt produces optimal solutions as indicated by references proposing respective algorithms.