The Cloud computing is the widely spread model of utility-computing that proposes an on-demand internet-based access to configurable resources available within data centers. If public providers enable simple access to resources for companies and users who have sporadic computation or storage needs, private clouds could sometimes be preferred for security or privacy reasons, or for cost reasons due to a high frequency usage of services. However, in many cases a choice between public or private clouds does not fulfill all requirements of companies and hybrid cloud infrastructures should be preferred. A hybrid cloud solution could, for example, answer sudden workload increase, security or fault tolerance requirements, or even latency issues thanks to data-locality. Solutions have already been proposed to address hybrid cloud infrastructures, however most of the time the placement of a distributed software on such infrastructure has to be indicated manually. For this reason, the automation of software deployment on hybrid clouds is still under research. The placement problem is a NP-hard multi-dimensional and multi-objective bin-packing problem. In this paper we propose new specific placement constraints and objectives adapted to hybrid clouds infrastructures in our solution OptiPlace, and we address this problem through constraint programming. Furthermore we evaluate the expressivity and performance of the proposed solution on a real case study.