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• This article provides an optimization approach to determine the optimal wind power plant sizes for selected districts in Madhya Pradesh, India. • It utilises a multi-objective genetic algorithm to estimate wind power plant size. • The selected plant installation sites have a minimum capacity utilisation factor (CUF) of 19%. • Minimization of demand generation gap, unit cost of energy generated, and transmission and distribution losses in chosen districts are the objectives. • Calculations indicate that the suggested algorithm can handle both basic and complex real-world scenarios with accurate and valid representation. Wind speed forecasting is primary to the dispatching and controllability of the power grid. This paper presents an optimization technique to estimate the optimal size of wind power plants required to fulfill the varying load demand of different districts in the state of Madhya Pradesh, India. The districts were selected based on the wind Capacity Utilization Factor (CUF) and land availability. This article proposes construction of small wind power plants in each district in order to satisfy local energy needs and, if necessary, serve the neighboring districts, thus reducing the dependence on the grid. The losses caused during transmission and distribution are substantially reduced. This article highlights the issue of estimating the size of wind power plants as an objective problem of optimization. The estimate of the energy plant size is considered a multi-objective optimising issue, and three scenarios are chosen as objectives. The first case is to reduce the monthly difference between energy demand and production in every area. In the second case, the cost of each unit generated is minimised. The third case involves reducing the power supply from one district to the other's losses in transmission and distribution. This multi-objective problem is solved via the genetic algorithm. The aim is to minimise the RMS value of demand inaccuracy by considering the cost of power generated, which was reduced at least to INR 3.60. The simulation of the optimization approach suggested indicates that the algorithm's plant size closely follows the targets. [ABSTRACT FROM AUTHOR]