This paper carries out load profiling of Singapore housing units and office buildings using bottom-up method for peak load reduction through optimization. Housing units in Singapore are classified according to the number of rooms, ranging from one to five. Average daily, monthly and yearly electrical usage of all these units is obtained. To explore the strategies for peak load reduction, a mathematical model based on bottom-up method is created. Statistical data — hourly probability factors, frequency of daily operations, saturation levels and nominal wattage of appliances — are gathered for typical Singapore households, and load profiles are generated. The accuracy of the bottom-up model is verified by comparing the load data of a typical three-rooms housing unit in Nanyang Technological University. Then, the problem of peak load reduction is formulated into an optimization problem with peak load as cost function and hourly probability factors of certain appliances as decision vaiables. Solution to the optimal hourly probability factors is obtained using genetic algorithm and it is demonstrated through numerical simulations that about 40% reduction in peak load can be achieved. Finally, some statistical information on load estimation for office buildings is presented.