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Selection of the right user stories and planning their implementation for the next iteration is critical for success of extreme Programming (XP). Success here is measured by the total business value generated from all user stories implemented within time. The business value of an iteration is composed by the value of the individual user stories selected and additional value created from themes of user stories. In this paper, a method combining advanced search and risk analysis is proposed to support decision-making for the 芒聙聹best芒聙聺 set of user stories. The advanced search technique combines genetic search with subsequent application of the hill climbing technique. The top candidate solutions are further analyzed pro-actively in terms of their risk to be implementable in-time with the available effort. As a proof-of-concept, the applicability of the proposed method is applied for the planning of one iteration in a case study project with 40 user stories. As a result, a set of trade-off solutions is offered as decision support for XP teams.