Optimisation of power generation and plant capacity are of primary significance for the
improvement of power supplies, cost decisions and economics. This paper develops a robust predictive
model for electric power generation and capacity utilisation and integrates the output from predictive
models into a multi-objective model. The optimal solution was determined after comparing the
performance of a Real Coding Genetic Algorithm (RCGA), Particle Swarm Optimisation (PSO) and Big-
Bang Big-Crunch Algorithm (BB-BC). Testing of the proposed model was carried out using data from a
Nigerian electric power generation plant with a capacity of about 5 million Megawatt Hours (MWH) to
test the presented methodology. Our findings indicate that the Auto-Regressive Integrated Moving
average (ARIMA) model adequately predicts the power generation variables and compares favorably
with literature results. The RCGA performed better than the BB-BC and PSO algorithms in terms of the
quality of solutions for the proposed model. The outcome of this study suggests that computational
complexity can be reduced in the evaluation of variables, yet producing a practical, simple and robust
model