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Cheap and clean energy demand is continuously increasing due to economic growth and industrialization. The energy sectors of several countries still employ fossil fuels for power production and there is a concern of associated emissions of greenhouse gases (GHG). On the other hand, environmental regulations are becoming more stringent, and resultant emissions need to be mitigated. Therefore, optimal energy policies considering economic resources and environmentally friendly pathways for electricity generation are essential. The objective of this paper is to develop a comprehensive model to optimize the power sector. For this purpose, a multi-period mixed integer programming (MPMIP) model was developed in a General Algebraic Modeling System (GAMS) to minimize the cost of electricity and reduce carbon dioxide (CO₂) emissions. Various CO₂ mitigation strategies such as fuel balancing and carbon capture and sequestration (CCS) were employed. The model was tested on a case study from Pakistan for a period of 13 years from 2018 to 2030. All types of power plants were considered that are available and to be installed from 2018 to 2030. Moreover, capacity expansion was also considered where needed. Fuel balancing was found to be the most suitable and promising option for CO₂ mitigation as up to 40% CO₂ mitigation can be achieved by the year 2030 starting from 4% in 2018 for all scenarios without increase in the cost of electricity (COE). CO₂ mitigation higher than 40% by the year 2030 can also be realized but the number of new proposed power plants was much higher beyond this target, which resulted in increased COE. Implementation of carbon capture and sequestration (CCS) on new power plants also reduced the CO₂ emissions considerably with an increase in COE of up to 15%.