The solution of large combinatorial optimization problems is becoming increasingly important in diverse areas of chemical engineering such as batch process design and scheduling, molecular simulation, and process control. Branch and bound is a well established framework that is at the core of existing methods for rigorously solving hard combinatorial optimization problems. Parallel and distributed computers offer great promise in reducing the execution times of branch and bound computations. However, the time and effort needed to parallelize algorithms exacerbates the already arduous task of algorithm development. This has prevented the routine use of parallel and distributed computers in solving combinatorial optimization problems. In this paper we discuss the development of a tool that is aimed at reducing the burden associated with designing and implementing branch and bound algorithms in a distributed environment. The design goal of DCABB is to automate the implementation aspects of the distributed algorithm without imposing rigid protocol formats that restrict the customizability of the algorithm. Algorithm flexibility is achieved by allowing user written modules to customize various components of a branch and bound algorithm. The tool consists of a programmer interface and a runtime environment. Since the tool is aimed towards a network environment, workload balancing, processor connectivity and robustness of the system with respect to machine and/or network failures are important issues. We report computational results to show the viability of the DCABB parallelization paradigm.