In this paper, we propose a distributed optimization algorithm for the demand response management with a comprehensive customer demand modeling framework in the smart grid. Different from the existing literature, the considered demand modeling framework considers not only the energy management modeling but also the appliance-level usage pattern learning models, both for time-shiftable loads. More specific, a bill minimization based demand optimization model is firstly proposed for the customers choosing to use a home energy management software. Secondly, an appliance level probability behaviour model via calculating the probability distribution of different electricity consumption patterns in response to the dynamic prices is proposed for the customers choosing to manage their energy usages by themselves. Based on the optimization and learning results, we further propose a multi-population genetic algorithm based pricing optimization model for demand response management with the aim to maximize the retailer's profit and maximize customers' benefits. Numerical results indicate the applicability and effectiveness of the proposed models and its benefits.